diff --git a/Dockerfile b/Dockerfile
index ecf0dbbb..5fe801a7 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -50,4 +50,4 @@ COPY nginx.conf /etc/nginx/nginx.conf
 EXPOSE 80
 
 # Start the servers
-CMD ["/bin/bash", "-c", "ollama serve & service nginx start && service redis-server start && node /app/start.js"]
\ No newline at end of file
+CMD ["/bin/bash", "/app/docker-start.sh"]
\ No newline at end of file
diff --git a/Dockerfile.dev b/Dockerfile.dev
index 1bf2c4db..664eb16a 100644
--- a/Dockerfile.dev
+++ b/Dockerfile.dev
@@ -32,6 +32,8 @@ RUN npm install
 # Install chrome for puppeteer
 RUN npx puppeteer browsers install chrome --install-deps
 
+RUN chmod -R 777 /node_dependencies
+
 # Copy nginx configuration
 COPY nginx.conf /etc/nginx/nginx.conf
 
@@ -39,10 +41,4 @@ COPY nginx.conf /etc/nginx/nginx.conf
 EXPOSE 80 3000 8000
 
 # Start the servers
-CMD ["/bin/bash", "-c", "\
-  rm -rf /app/servers/nextjs/node_modules && \
-  ln -s /node_dependencies/node_modules /app/servers/nextjs/node_modules && \
-  ollama serve & \
-  service nginx start & \
-  service redis-server start && \
-  node /app/start.js"]
\ No newline at end of file
+CMD ["/bin/bash", "/app/docker-dev-start.sh"]
\ No newline at end of file
diff --git a/docker-compose.yml b/docker-compose.yml
index 52262bf0..49cf6c49 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -48,7 +48,4 @@ services:
       - OPENAI_API_KEY=${OPENAI_API_KEY}
       - GOOGLE_API_KEY=${GOOGLE_API_KEY}
       - OLLAMA_MODEL=${OLLAMA_MODEL}
-      - PEXELS_API_KEY=${PEXELS_API_KEY}
-      - LANGCHAIN_TRACING_V2=${LANGCHAIN_TRACING_V2}
-      - LANGCHAIN_API_KEY=${LANGCHAIN_API_KEY}
-      - LANGCHAIN_PROJECT=${LANGCHAIN_PROJECT}
\ No newline at end of file
+      - PEXELS_API_KEY=${PEXELS_API_KEY}
\ No newline at end of file
diff --git a/docker-dev-start.sh b/docker-dev-start.sh
new file mode 100644
index 00000000..bd6ae2ad
--- /dev/null
+++ b/docker-dev-start.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+
+echo "Starting development server..."
+
+if [ -d "/node_dependencies/node_modules" ]; then
+  rm -rf /app/servers/nextjs/node_modules
+  mv /node_dependencies/node_modules /app/servers/nextjs
+fi
+
+ollama serve &
+service nginx start
+service redis-server start
+node /app/start.js
diff --git a/docker-start.sh b/docker-start.sh
new file mode 100644
index 00000000..22c80b91
--- /dev/null
+++ b/docker-start.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+
+echo "Starting production server..."
+
+ollama serve &
+service nginx start
+service redis-server start
+node /app/start.js
diff --git a/servers/fastapi/api/routers/presentation/handlers/generate_stream.py b/servers/fastapi/api/routers/presentation/handlers/generate_stream.py
index 78d44aa5..d0125477 100644
--- a/servers/fastapi/api/routers/presentation/handlers/generate_stream.py
+++ b/servers/fastapi/api/routers/presentation/handlers/generate_stream.py
@@ -24,7 +24,6 @@ from ppt_config_generator.models import (
     PresentationStructureModel,
 )
 from ppt_generator.generator import generate_presentation_stream
-from ppt_generator.models.content_type_models import CONTENT_TYPE_MAPPING
 from ppt_generator.models.llm_models import (
     LLM_CONTENT_TYPE_MAPPING,
     LLMPresentationModel,
@@ -145,18 +144,21 @@ class PresentationGenerateStreamHandler(FetchAssetsOnPresentationGenerationMixin
 
     async def generate_presentation_openai_google(self):
         presentation_text = ""
-        async for chunk in generate_presentation_stream(
+        async for event in await generate_presentation_stream(
             PresentationMarkdownModel(
                 title=self.title,
                 slides=self.outlines,
                 notes=self.presentation.notes,
             )
         ):
-            presentation_text += chunk.content
-            yield SSEResponse(
-                event="response",
-                data=json.dumps({"type": "chunk", "chunk": chunk.content}),
-            ).to_string()
+            print(event)
+            print("-" * 100)
+        return
+        # presentation_text += event
+        yield SSEResponse(
+            event="response",
+            data=json.dumps({"type": "chunk", "chunk": chunk}),
+        ).to_string()
 
         self.presentation_json = output_parser.parse(presentation_text)
 
diff --git a/servers/fastapi/api/utils/utils.py b/servers/fastapi/api/utils/utils.py
index 16b434d0..975b751d 100644
--- a/servers/fastapi/api/utils/utils.py
+++ b/servers/fastapi/api/utils/utils.py
@@ -129,35 +129,35 @@ async def download_files(urls: List[str], save_paths: List[str]):
 async def handle_errors(
     func, logging_service: LoggingService, log_metadata: LogMetadata, **kwargs
 ):
-    try:
-        logging_service.logger.info(f"START", extra=log_metadata.model_dump())
-        response = await func(
-            logging_service=logging_service, log_metadata=log_metadata, **kwargs
-        )
-        is_stream = isinstance(response, StreamingResponse)
-        logging_service.logger.info(
-            "STREAMING" if is_stream else "END", extra=log_metadata.model_dump()
-        )
-        return response
+    # try:
+    logging_service.logger.info(f"START", extra=log_metadata.model_dump())
+    response = await func(
+        logging_service=logging_service, log_metadata=log_metadata, **kwargs
+    )
+    is_stream = isinstance(response, StreamingResponse)
+    logging_service.logger.info(
+        "STREAMING" if is_stream else "END", extra=log_metadata.model_dump()
+    )
+    return response
 
-    except HTTPException as e:
-        log_metadata.status_code = e.status_code
-        logging_service.logger.error(
-            f"Raised HTTPException - {e.detail}", extra=log_metadata.model_dump()
-        )
-        raise e
-    except Exception as e:
-        print(traceback.print_stack())
-        print(traceback.print_exc())
+    # except HTTPException as e:
+    #     log_metadata.status_code = e.status_code
+    #     logging_service.logger.error(
+    #         f"Raised HTTPException - {e.detail}", extra=log_metadata.model_dump()
+    #     )
+    #     raise e
+    # except Exception as e:
+    #     print(traceback.print_stack())
+    #     print(traceback.print_exc())
 
-        log_metadata.status_code = 400
-        logging_service.logger.critical(
-            "Unhandled Exception",
-            exc_info=True,
-            stack_info=True,
-            extra=log_metadata.model_dump(),
-        )
-        raise HTTPException(400, "Something went wrong while processing your request.")
+    #     log_metadata.status_code = 400
+    #     logging_service.logger.critical(
+    #         "Unhandled Exception",
+    #         exc_info=True,
+    #         stack_info=True,
+    #         extra=log_metadata.model_dump(),
+    #     )
+    #     raise HTTPException(400, "Something went wrong while processing your request.")
 
 
 def sanitize_filename(filename: str) -> str:
diff --git a/servers/fastapi/graph_processor/models.py b/servers/fastapi/graph_processor/models.py
deleted file mode 100644
index 6ced32b1..00000000
--- a/servers/fastapi/graph_processor/models.py
+++ /dev/null
@@ -1,132 +0,0 @@
-from enum import Enum
-from typing import List, Optional
-from pydantic import BaseModel, Field, model_validator
-
-from graph_processor.utils import clip_text
-
-
-class PointModel(BaseModel):
-    x: float
-    y: float
-
-    def to_list(self) -> List[float]:
-        return [self.x, self.y]
-
-
-class PointWithRadius(PointModel):
-    radius: Optional[float] = None
-
-
-class BarSeriesModel(BaseModel):
-    name: str
-    data: List[float] = Field(
-        description="Only numbers should be given out in data. Don't include text/string in data."
-    )
-
-    def get_name(self) -> str:
-        return clip_text(self.name)
-
-
-class ScatterSeriesModel(BaseModel):
-    name: str
-    points: List[PointModel]
-
-    def get_name(self) -> str:
-        return clip_text(self.name)
-
-
-class BubbleSeriesModel(BaseModel):
-    name: str
-    points: List[PointWithRadius]
-
-    def get_name(self) -> str:
-        return clip_text(self.name)
-
-
-class LineSeriesModel(BaseModel):
-    name: str
-    data: List[float] = Field(
-        description="Only numbers should be given out in data. Don't include text/string in data."
-    )
-
-    def get_name(self) -> str:
-        return clip_text(self.name)
-
-
-class PieChartSeriesModel(BaseModel):
-    data: List[float]
-
-
-class BarGraphDataModel(BaseModel):
-    categories: List[str]
-    series: List[BarSeriesModel] = Field(
-        description="There should be no more than 3 series"
-    )
-
-    def get_categories(self) -> List[str]:
-        return [clip_text(category) for category in self.categories]
-
-
-class ScatterChartDataModel(BaseModel):
-    series: List[ScatterSeriesModel]
-
-
-class BubbleChartDataModel(BaseModel):
-    series: List[BubbleSeriesModel]
-
-
-class LineChartDataModel(BaseModel):
-    categories: List[str]
-    series: List[LineSeriesModel] = Field(
-        description="There should be no more than 3 series"
-    )
-
-    def get_categories(self) -> List[str]:
-        return [clip_text(category) for category in self.categories]
-
-
-class PieChartDataModel(BaseModel):
-    categories: List[str]
-    series: List[PieChartSeriesModel] = Field(
-        description="One series model with list of data",
-        min_length=1,
-    )
-
-    @model_validator(mode="after")
-    def limit_series(self):
-        self.series = self.series[:1]
-        return self
-
-    def get_categories(self) -> List[str]:
-        return [clip_text(category) for category in self.categories]
-
-
-# class TableDataModel(BaseModel):
-#     categories: List[str]
-#     series: List[BarSeriesModel]
-
-#     def get_categories(self) -> List[str]:
-#         return [clip_text(category) for category in self.categories]
-
-
-class GraphTypeEnum(Enum):
-    pie = "pie"
-    bar = "bar"
-    line = "line"
-
-
-class GraphModel(BaseModel):
-    style: Optional[dict] = {}
-    name: str
-    type: GraphTypeEnum
-    unit: Optional[str] = Field(
-        description="Unit of the data in the graph. Example: %, kg, million USD, tonnes, etc."
-    )
-    data: PieChartDataModel | LineChartDataModel | BarGraphDataModel
-
-
-GRAPH_TYPE_MAPPING = {
-    GraphTypeEnum.pie: PieChartDataModel,
-    GraphTypeEnum.bar: BarGraphDataModel,
-    GraphTypeEnum.line: LineChartDataModel,
-}
diff --git a/servers/fastapi/graph_processor/utils.py b/servers/fastapi/graph_processor/utils.py
deleted file mode 100644
index 502145f0..00000000
--- a/servers/fastapi/graph_processor/utils.py
+++ /dev/null
@@ -1,3 +0,0 @@
-def clip_text(text: str, max_length: int = 6) -> str:
-    # return text[:max_length] + ".." if len(text) > max_length else text
-    return text
diff --git a/servers/fastapi/ppt_config_generator/ppt_outlines_generator.py b/servers/fastapi/ppt_config_generator/ppt_outlines_generator.py
index 3bfc17f3..06b5c1ce 100644
--- a/servers/fastapi/ppt_config_generator/ppt_outlines_generator.py
+++ b/servers/fastapi/ppt_config_generator/ppt_outlines_generator.py
@@ -1,33 +1,17 @@
 from typing import Optional
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_ollama import ChatOllama
 
-from api.utils.model_utils import get_large_model
+from api.utils.model_utils import get_large_model, get_llm_client
 from api.utils.variable_length_models import (
     get_presentation_markdown_model_with_n_slides,
 )
 from ppt_config_generator.models import PresentationMarkdownModel
-from ppt_generator.fix_validation_errors import get_validated_response
 
 
-user_prompt_text = {
-    "type": "text",
-    "text": """
-                **Input:**
-                - Prompt: {prompt}
-                - Output Language: {language}
-                - Number of Slides: {n_slides}
-                - Additional Information: {content}
-            """,
-}
-
-
-def get_prompt_template():
-    return ChatPromptTemplate.from_messages(
-        [
-            (
-                "system",
-                """
+def get_prompt_template(prompt: str, n_slides: int, language: str, content: str):
+    return [
+        {
+            "role": "system",
+            "content": """
                 Create a presentation based on the provided prompt, number of slides, output language, and additional informational details.
                 Format the output in the specified JSON schema with structured markdown content.
     
@@ -50,13 +34,18 @@ def get_prompt_template():
                 - Slide **title** should not be in markdown format.
                 - There must be exact **Number of Slides** as specified.
                 """,
-            ),
-            (
-                "user",
-                [user_prompt_text],
-            ),
-        ],
-    )
+        },
+        {
+            "role": "user",
+            "content": f"""
+                **Input:**
+                - Prompt: {prompt}
+                - Output Language: {language}
+                - Number of Slides: {n_slides}
+                - Additional Information: {content}
+            """,
+        },
+    ]
 
 
 async def generate_ppt_content(
@@ -65,21 +54,14 @@ async def generate_ppt_content(
     language: Optional[str] = None,
     content: Optional[str] = None,
 ) -> PresentationMarkdownModel:
-    model = ChatOllama(model=get_large_model(), temperature=0.8)
+    client = get_llm_client()
+    model = get_large_model()
     response_model = get_presentation_markdown_model_with_n_slides(n_slides)
 
-    chain = get_prompt_template() | model.with_structured_output(
-        response_model.model_json_schema()
-    )
-
-    return await get_validated_response(
-        chain,
-        {
-            "prompt": prompt,
-            "n_slides": n_slides,
-            "language": language or "English",
-            "content": content,
-        },
-        response_model,
-        PresentationMarkdownModel,
+    response = await client.beta.chat.completions.parse(
+        model=model,
+        temperature=0.2,
+        messages=get_prompt_template(prompt, n_slides, language, content),
+        response_format=response_model,
     )
+    return response.choices[0].message.parsed
diff --git a/servers/fastapi/ppt_config_generator/structure_generator.py b/servers/fastapi/ppt_config_generator/structure_generator.py
index 98275a6b..9e0f4972 100644
--- a/servers/fastapi/ppt_config_generator/structure_generator.py
+++ b/servers/fastapi/ppt_config_generator/structure_generator.py
@@ -1,7 +1,4 @@
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_ollama import ChatOllama
-
-from api.utils.model_utils import get_small_model
+from api.utils.model_utils import get_llm_client, get_small_model
 from api.utils.variable_length_models import (
     get_presentation_structure_model_with_n_slides,
 )
@@ -9,13 +6,13 @@ from ppt_config_generator.models import (
     PresentationStructureModel,
     PresentationMarkdownModel,
 )
-from ppt_generator.fix_validation_errors import get_validated_response
 
-prompt = ChatPromptTemplate.from_messages(
-    [
-        (
-            "system",
-            """
+
+def get_prompt(n_slides: int, data: str):
+    return [
+        {
+            "role": "system",
+            "content": f"""
                 You're a professional presentation designer with years of experience in designing clear and engaging presentations.
 
                 # Slide Types
@@ -45,33 +42,32 @@ prompt = ChatPromptTemplate.from_messages(
 
                 **Go through notes and steps and make sure they are all followed. Rule breaks are strictly not allowed.**
             """,
-        ),
-        (
-            "human",
-            """
-            {data}
+        },
+        {
+            "role": "user",
+            "content": f"""
+                {data}
             """,
-        ),
+        },
     ]
-)
 
 
 async def generate_presentation_structure(
     presentation_outline: PresentationMarkdownModel,
 ) -> PresentationStructureModel:
 
-    model = ChatOllama(model=get_small_model(), temperature=0.8)
+    client = get_llm_client()
+    model = get_small_model()
     response_model = get_presentation_structure_model_with_n_slides(
         len(presentation_outline.slides)
     )
-    chain = prompt | model.with_structured_output(response_model.model_json_schema())
 
-    return await get_validated_response(
-        chain,
-        {
-            "n_slides": len(presentation_outline.slides),
-            "data": presentation_outline.to_string(),
-        },
-        response_model,
-        PresentationStructureModel,
+    response = await client.beta.chat.completions.parse(
+        model=model,
+        temperature=0.2,
+        messages=get_prompt(
+            len(presentation_outline.slides), presentation_outline.to_string()
+        ),
+        response_format=response_model,
     )
+    return response.choices[0].message.parsed
diff --git a/servers/fastapi/ppt_generator/generator.py b/servers/fastapi/ppt_generator/generator.py
index 82ba9bf5..fd44ec18 100644
--- a/servers/fastapi/ppt_generator/generator.py
+++ b/servers/fastapi/ppt_generator/generator.py
@@ -1,16 +1,8 @@
 from typing import AsyncIterator
 
-from langchain_core.messages import (
-    HumanMessage,
-    AIMessageChunk,
-    AIMessage,
-)
-from langchain_ollama import ChatOllama
-from api.utils.model_utils import get_large_model
+from api.utils.model_utils import get_large_model, get_llm_client
 from ppt_config_generator.models import PresentationMarkdownModel
-from ppt_generator.models.llm_models_with_validations import (
-    LLMPresentationModelWithValidation,
-)
+from ppt_generator.models.llm_models import LLMPresentationModel
 
 
 CREATE_PRESENTATION_PROMPT = """
@@ -71,42 +63,56 @@ CREATE_PRESENTATION_PROMPT = """
     **Go through notes and steps and make sure they are all followed. Rule breaks are strictly not allowed.**
 """
 
-schema = LLMPresentationModelWithValidation.model_json_schema()
+# schema = LLMPresentationModel.model_json_schema()
 
-system_prompt = f"""
-{CREATE_PRESENTATION_PROMPT}
+# system_prompt = f"""
+# {CREATE_PRESENTATION_PROMPT}
 
-Follow this schema while giving out response: {schema}.
+# Follow this schema while giving out response: {schema}.
 
-Make description short and obey the character limits. Output should be in JSON format. Give out only JSON, nothing else.
-"""
+# Make description short and obey the character limits. Output should be in JSON format. Give out only JSON, nothing else.
+# """
 
-ollama_system_prompt = f"""
-{CREATE_PRESENTATION_PROMPT}
+# ollama_system_prompt = f"""
+# {CREATE_PRESENTATION_PROMPT}
 
-Make description short and obey the character limits. Output should be in JSON format. Give out only JSON, nothing else.
-"""
+# Make description short and obey the character limits. Output should be in JSON format. Give out only JSON, nothing else.
+# """
 
 
-def get_model_and_messages(
+async def generate_presentation_stream(
     presentation_outline: PresentationMarkdownModel,
 ):
-    user_message = HumanMessage(presentation_outline.to_string())
-    model = ChatOllama(model=get_large_model(), temperature=0.8)
+    client = get_llm_client()
+    model = get_large_model()
 
-    return model, system_prompt, user_message
-
-
-def generate_presentation_stream(
-    presentation_outline: PresentationMarkdownModel,
-) -> AsyncIterator[AIMessageChunk]:
-    model, system_prompt, user_message = get_model_and_messages(presentation_outline)
-
-    return model.astream([system_prompt, user_message])
+    response = await client.chat.completions.create(
+        model=model,
+        messages=[
+            {
+                "role": "system",
+                "content": CREATE_PRESENTATION_PROMPT,
+            },
+            {
+                "role": "user",
+                "content": presentation_outline.to_string(),
+            },
+        ],
+        response_format={
+            "type": "json_schema",
+            "json_schema": {
+                "name": "LLMPresentationModel",
+                "schema": LLMPresentationModel.model_json_schema(),
+            },
+        },
+        stream=True,
+    )
+    return response
 
 
 async def generate_presentation(
     presentation_outline: PresentationMarkdownModel,
-) -> AIMessage:
-    model, system_prompt, user_message = get_model_and_messages(presentation_outline)
-    return await model.ainvoke([system_prompt, user_message])
+):
+    # model, system_prompt, user_message = get_model_and_messages(presentation_outline)
+    # return await model.ainvoke([system_prompt, user_message])
+    pass
diff --git a/servers/fastapi/ppt_generator/models/content_type_models.py b/servers/fastapi/ppt_generator/models/content_type_models.py
index f13cb76c..4d6b1513 100644
--- a/servers/fastapi/ppt_generator/models/content_type_models.py
+++ b/servers/fastapi/ppt_generator/models/content_type_models.py
@@ -12,7 +12,17 @@ from ppt_generator.models.other_models import (
     TYPE8,
     TYPE9,
 )
-from graph_processor.models import GraphModel
+
+
+class TableDataModel(BaseModel):
+    x_labels: List[str]
+    y_labels: List[str]
+    data: List[List[float]]
+
+
+class TableModel(BaseModel):
+    name: str
+    data: TableDataModel
 
 
 class HeadingModel(BaseModel):
@@ -110,7 +120,7 @@ class Type4Content(SlideContentModel):
 
 class Type5Content(SlideContentModel):
     body: str
-    graph: GraphModel
+    table: TableModel
 
     def to_llm_content(self):
         from ppt_generator.models.llm_models import LLMType5Content
@@ -118,7 +128,7 @@ class Type5Content(SlideContentModel):
         return LLMType5Content(
             title=self.title,
             body=self.body,
-            graph=self.graph,
+            table=self.table,
         )
 
 
@@ -174,7 +184,7 @@ class Type8Content(SlideContentModel):
 
 class Type9Content(SlideContentModel):
     body: List[HeadingModel]
-    graph: GraphModel
+    table: TableModel
 
     def to_llm_content(self):
         from ppt_generator.models.llm_models import LLMType9Content
@@ -182,7 +192,7 @@ class Type9Content(SlideContentModel):
         return LLMType9Content(
             title=self.title,
             body=[item.to_llm_content() for item in self.body],
-            graph=self.graph,
+            table=self.table,
         )
 
 
diff --git a/servers/fastapi/ppt_generator/models/llm_models.py b/servers/fastapi/ppt_generator/models/llm_models.py
index 1ff43feb..a08292e8 100644
--- a/servers/fastapi/ppt_generator/models/llm_models.py
+++ b/servers/fastapi/ppt_generator/models/llm_models.py
@@ -1,10 +1,11 @@
-from typing import List, Mapping
-from pydantic import BaseModel
+from typing import List, Literal, Mapping
+from pydantic import BaseModel, Field
 
-from graph_processor.models import GraphModel
 from ppt_generator.models.content_type_models import (
     HeadingModel,
     SlideContentModel,
+    TableDataModel,
+    TableModel,
     Type1Content,
     Type2Content,
     Type3Content,
@@ -28,9 +29,24 @@ from ppt_generator.models.other_models import (
 )
 
 
+class LLMTableDataModel(TableDataModel):
+    x_labels: List[str] = Field(description="X labels of the table")
+    y_labels: List[str] = Field(description="Y labels of the table")
+    data: List[List[float]] = Field(description="Data of the table")
+
+
+class LLMTableModel(TableModel):
+    name: str = Field(description="Name of the table")
+    data: LLMTableDataModel
+
+
 class LLMHeadingModel(BaseModel):
-    heading: str
-    description: str
+    heading: str = Field(
+        description="Item heading in less than 6 words",
+    )
+    description: str = Field(
+        description="Item description in less than 15 words.",
+    )
 
     def to_content(self) -> HeadingModel:
         return HeadingModel(
@@ -40,23 +56,46 @@ class LLMHeadingModel(BaseModel):
 
 
 class LLMHeadingModelWithImagePrompt(LLMHeadingModel):
-    image_prompt: str
+    image_prompt: str = Field(
+        description="Item image prompt in less than 5 words",
+    )
+
+    def to_content(self) -> HeadingModel:
+        return HeadingModel(
+            heading=self.heading,
+            description=self.description,
+        )
 
 
 class LLMHeadingModelWithIconQuery(LLMHeadingModel):
-    icon_query: str
+    icon_query: str = Field(
+        description="Item icon query in less than 5 words",
+    )
+
+    def to_content(self) -> HeadingModel:
+        return HeadingModel(
+            heading=self.heading,
+            description=self.description,
+        )
 
 
 class LLMSlideContentModel(BaseModel):
-    title: str
+    # title: str = Field(
+    #     description="Slide title in less than 8 words",
+    # )
 
     def to_content(self) -> SlideContentModel:
         raise NotImplementedError("to_content method not implemented")
 
 
 class LLMType1Content(LLMSlideContentModel):
-    body: str
-    image_prompt: str
+    type: Literal["1"] = "1"
+    body: str = Field(
+        description="Slide content summary in less than 30 words.",
+    )
+    image_prompt: str = Field(
+        description="Slide image prompt in less than 5 words",
+    )
 
     def to_content(self) -> Type1Content:
         return Type1Content(
@@ -67,7 +106,12 @@ class LLMType1Content(LLMSlideContentModel):
 
 
 class LLMType2Content(LLMSlideContentModel):
-    body: List[LLMHeadingModel]
+    type: Literal["2"] = "2"
+    body: List[LLMHeadingModel] = Field(
+        description="Items to show in slide",
+        min_length=1,
+        max_length=4,
+    )
 
     def to_content(self) -> Type2Content:
         return Type2Content(
@@ -77,8 +121,15 @@ class LLMType2Content(LLMSlideContentModel):
 
 
 class LLMType3Content(LLMSlideContentModel):
-    body: List[LLMHeadingModel]
-    image_prompt: str
+    type: Literal["3"] = "3"
+    body: List[LLMHeadingModel] = Field(
+        description="Items to show in slide",
+        min_length=3,
+        max_length=3,
+    )
+    image_prompt: str = Field(
+        description="Slide image prompt in less than 5 words",
+    )
 
     def to_content(self) -> Type3Content:
         return Type3Content(
@@ -89,7 +140,12 @@ class LLMType3Content(LLMSlideContentModel):
 
 
 class LLMType4Content(LLMSlideContentModel):
-    body: List[LLMHeadingModelWithImagePrompt]
+    type: Literal["4"] = "4"
+    body: List[LLMHeadingModelWithImagePrompt] = Field(
+        description="Items to show in slide",
+        min_length=1,
+        max_length=3,
+    )
 
     def to_content(self) -> Type4Content:
         return Type4Content(
@@ -100,20 +156,30 @@ class LLMType4Content(LLMSlideContentModel):
 
 
 class LLMType5Content(LLMSlideContentModel):
-    body: str
-    graph: GraphModel
+    type: Literal["5"] = "5"
+    body: str = Field(
+        description="Slide content summary in less than 30 words.",
+    )
+    table: LLMTableModel = Field(description="Table to show in slide")
 
     def to_content(self) -> Type5Content:
         return Type5Content(
             title=self.title,
             body=self.body,
-            graph=self.graph,
+            table=self.table,
         )
 
 
 class LLMType6Content(LLMSlideContentModel):
-    description: str
-    body: List[LLMHeadingModel]
+    type: Literal["6"] = "6"
+    description: str = Field(
+        description="Slide content summary in less than 20 words.",
+    )
+    body: List[LLMHeadingModel] = Field(
+        description="Items to show in slide",
+        min_length=1,
+        max_length=3,
+    )
 
     def to_content(self) -> Type6Content:
         return Type6Content(
@@ -124,7 +190,12 @@ class LLMType6Content(LLMSlideContentModel):
 
 
 class LLMType7Content(LLMSlideContentModel):
-    body: List[LLMHeadingModelWithIconQuery]
+    type: Literal["7"] = "7"
+    body: List[LLMHeadingModelWithIconQuery] = Field(
+        description="Items to show in slide",
+        min_length=1,
+        max_length=4,
+    )
 
     def to_content(self) -> Type7Content:
         return Type7Content(
@@ -135,8 +206,15 @@ class LLMType7Content(LLMSlideContentModel):
 
 
 class LLMType8Content(LLMSlideContentModel):
-    description: str
-    body: List[LLMHeadingModelWithImagePrompt]
+    type: Literal["8"] = "8"
+    description: str = Field(
+        description="Slide content summary in less than 20 words.",
+    )
+    body: List[LLMHeadingModelWithImagePrompt] = Field(
+        description="Items to show in slide",
+        min_length=1,
+        max_length=3,
+    )
 
     def to_content(self) -> Type8Content:
         return Type8Content(
@@ -148,14 +226,19 @@ class LLMType8Content(LLMSlideContentModel):
 
 
 class LLMType9Content(LLMSlideContentModel):
-    body: List[LLMHeadingModel]
-    graph: GraphModel
+    type: Literal["9"] = "9"
+    body: List[LLMHeadingModel] = Field(
+        description="Items to show in slide",
+        min_length=1,
+        max_length=3,
+    )
+    table: LLMTableModel = Field(description="Table to show in slide")
 
     def to_content(self) -> Type9Content:
         return Type9Content(
             title=self.title,
             body=[each.to_content() for each in self.body],
-            graph=self.graph,
+            table=self.table,
         )
 
 
@@ -180,9 +263,9 @@ class LLMSlideModel(BaseModel):
         | LLMType4Content
         | LLMType5Content
         | LLMType6Content
-        | LLMType7Content
-        | LLMType8Content
-        | LLMType9Content
+        # | LLMType7Content
+        # | LLMType8Content
+        # | LLMType9Content
     )
 
 
diff --git a/servers/fastapi/ppt_generator/models/llm_models_with_validations.py b/servers/fastapi/ppt_generator/models/llm_models_with_validations.py
deleted file mode 100644
index 638d853f..00000000
--- a/servers/fastapi/ppt_generator/models/llm_models_with_validations.py
+++ /dev/null
@@ -1,257 +0,0 @@
-from typing import List, Mapping
-from pydantic import Field
-
-from graph_processor.models import GraphModel
-from ppt_generator.models.other_models import (
-    TYPE1,
-    TYPE2,
-    TYPE3,
-    TYPE4,
-    TYPE5,
-    TYPE6,
-    TYPE7,
-    TYPE8,
-    TYPE9,
-)
-from ppt_generator.models.llm_models import (
-    LLMHeadingModel,
-    LLMHeadingModelWithImagePrompt,
-    LLMHeadingModelWithIconQuery,
-    LLMSlideContentModel,
-    LLMType1Content,
-    LLMType2Content,
-    LLMType3Content,
-    LLMType4Content,
-    LLMType5Content,
-    LLMType6Content,
-    LLMType7Content,
-    LLMType8Content,
-    LLMType9Content,
-    LLMSlideModel,
-    LLMPresentationModel,
-)
-
-
-class LLMHeadingModelWithValidation(LLMHeadingModel):
-    heading: str = Field(
-        description="List item heading to show in slide body in less than 5 words.",
-    )
-    description: str = Field(
-        description="Description of list item in less than 20 words.",
-    )
-
-
-class LLMHeadingModelWithImagePromptWithValidation(LLMHeadingModelWithImagePrompt):
-    image_prompt: str = Field(
-        description="Prompt used to generate image for this item in less than 6 words.",
-    )
-
-
-class LLMHeadingModelWithIconQueryWithValidation(LLMHeadingModelWithIconQuery):
-    icon_query: str = Field(
-        description="Icon query to generate icon for this item in less than 4 words.",
-    )
-
-
-class LLMType1ContentWithValidation(LLMType1Content):
-    title: str = Field(
-        description="Title of the slide in less than 6 words.",
-    )
-    body: str = Field(
-        description="Slide content summary in less than 30 words.",
-    )
-    image_prompt: str = Field(
-        description="Prompt used to generate image for this slide in less than 6 words.",
-    )
-
-    @classmethod
-    def get_notes(cls):
-        return ""
-
-
-class LLMType2ContentWithValidation(LLMType2Content):
-    title: str = Field(
-        description="Title of the slide in less than 6 words.",
-    )
-    body: List[LLMHeadingModelWithValidation] = Field(
-        description="List items to show in slide's body",
-        min_length=1,
-        max_length=4,
-    )
-
-    @classmethod
-    def get_notes(cls):
-        return """
-        - The **Body** should include **1 to 4 HeadingModels**.  
-        - Each **Heading** must consist of **1 to 3 words**.  
-        - Each item **Description** can be upto 10 words.
-        """
-
-
-class LLMType3ContentWithValidation(LLMType3Content):
-    title: str = Field(
-        description="Title of the slide in less than 6 words.",
-    )
-    body: List[LLMHeadingModelWithValidation] = Field(
-        description="List items to show in slide's body",
-        min_length=3,
-        max_length=3,
-    )
-    image_prompt: str = Field(
-        description="Prompt used to generate image for this slide in less than 6 words.",
-    )
-
-    @classmethod
-    def get_notes(cls):
-        return """
-        - The **Body** should include **3 HeadingModels**.  
-        - Each **Heading** must consist of **1 to 3 words**.  
-        - Each item **Description** can be upto 10 words.
-        """
-
-
-class LLMType4ContentWithValidation(LLMType4Content):
-    title: str = Field(
-        description="Title of the slide in less than 6 words.",
-    )
-    body: List[LLMHeadingModelWithImagePromptWithValidation] = Field(
-        description="List items to show in slide's body",
-        min_length=1,
-        max_length=3,
-    )
-
-    @classmethod
-    def get_notes(cls):
-        return """
-        - The **Body** should include **1 to 3 HeadingModels**.  
-        - Each **Heading** must consist of **1 to 3 words**.  
-        - Each item **Description** can be upto 10 words.
-        """
-
-
-class LLMType5ContentWithValidation(LLMType5Content):
-    title: str = Field(
-        description="Title of the slide in less than 6 words.",
-    )
-    body: str = Field(
-        description="Slide content summary in less than 30 words.",
-    )
-    graph: GraphModel = Field(description="Graph to show in slide")
-
-    @classmethod
-    def get_notes(self):
-        return ""
-
-
-class LLMType6ContentWithValidation(LLMType6Content):
-    title: str = Field(
-        description="Title of the slide in less than 6 words.",
-    )
-    description: str = Field(
-        description="Slide content summary in less than 20 words.",
-    )
-    body: List[LLMHeadingModelWithValidation] = Field(
-        description="List items to show in slide's body",
-        min_length=1,
-        max_length=3,
-    )
-
-    @classmethod
-    def get_notes(cls):
-        return """
-        - The **Body** should include **1 to 3 HeadingModels**.  
-        - Each **Heading** must consist of **1 to 3 words**.  
-        - Each item **Description** can be upto 10 words.
-        """
-
-
-class LLMType7ContentWithValidation(LLMType7Content):
-    title: str = Field(
-        description="Title of the slide in less than 6 words.",
-    )
-    body: List[LLMHeadingModelWithIconQueryWithValidation] = Field(
-        description="List items to show in slide's body",
-        min_length=1,
-        max_length=4,
-    )
-
-    @classmethod
-    def get_notes(cls):
-        return """
-        - The **Body** should include **1 to 4 HeadingModels**.  
-        - Each **Heading** must consist of **1 to 3 words**.  
-        - Each item **Description** can be upto 10 words.
-        """
-
-
-class LLMType8ContentWithValidation(LLMType8Content):
-    title: str = Field(
-        description="Title of the slide in less than 6 words.",
-    )
-    description: str = Field(
-        description="Slide content summary in less than 20 words.",
-    )
-    body: List[LLMHeadingModelWithImagePromptWithValidation] = Field(
-        description="List items to show in slide's body",
-        min_length=1,
-        max_length=3,
-    )
-
-    @classmethod
-    def get_notes(cls):
-        return """
-        - The **Body** should include **1 to 3 HeadingModels**.  
-        - Each **Heading** must consist of **1 to 3 words**.  
-        - Each item **Description** can be upto 10 words.
-        """
-
-
-class LLMType9ContentWithValidation(LLMType9Content):
-    title: str = Field(
-        description="Title of the slide in less than 6 words.",
-    )
-    body: List[LLMHeadingModelWithValidation] = Field(
-        description="List items to show in slide's body",
-        min_length=1,
-        max_length=3,
-    )
-    graph: GraphModel = Field(description="Graph to show in slide")
-
-    @classmethod
-    def get_notes(cls):
-        return """
-        - The **Body** should include **1 to 3 HeadingModels**.  
-        - Each **Heading** must consist of **1 to 3 words**.  
-        - Each item **Description** can be upto 10 words.
-        """
-
-
-LLM_CONTENT_TYPE_WITH_VALIDATION_MAPPING: Mapping[int, LLMSlideContentModel] = {
-    TYPE1: LLMType1ContentWithValidation,
-    TYPE2: LLMType2ContentWithValidation,
-    TYPE3: LLMType3ContentWithValidation,
-    TYPE4: LLMType4ContentWithValidation,
-    TYPE5: LLMType5ContentWithValidation,
-    TYPE6: LLMType6ContentWithValidation,
-    TYPE7: LLMType7ContentWithValidation,
-    TYPE8: LLMType8ContentWithValidation,
-    TYPE9: LLMType9ContentWithValidation,
-}
-
-
-class LLMSlideModelWithValidation(LLMSlideModel):
-    type: int
-    content: (
-        LLMType1ContentWithValidation
-        | LLMType2ContentWithValidation
-        | LLMType4ContentWithValidation
-        | LLMType5ContentWithValidation
-        | LLMType6ContentWithValidation
-        | LLMType7ContentWithValidation
-        | LLMType8ContentWithValidation
-        | LLMType9ContentWithValidation
-    )
-
-
-class LLMPresentationModelWithValidation(LLMPresentationModel):
-    slides: list[LLMSlideModelWithValidation]
diff --git a/servers/fastapi/ppt_generator/models/pptx_models.py b/servers/fastapi/ppt_generator/models/pptx_models.py
index 327f21dc..6808e6de 100644
--- a/servers/fastapi/ppt_generator/models/pptx_models.py
+++ b/servers/fastapi/ppt_generator/models/pptx_models.py
@@ -6,8 +6,6 @@ from pptx.util import Pt
 from pptx.enum.text import PP_ALIGN
 from pptx.enum.shapes import MSO_AUTO_SHAPE_TYPE, MSO_CONNECTOR_TYPE
 
-from graph_processor.models import GraphModel
-
 
 class PptxBoxShapeEnum(Enum):
     RECTANGLE = "rectangle"
@@ -138,14 +136,6 @@ class PptxPictureBoxModel(PptxShapeModel):
     picture: PptxPictureModel
 
 
-class PptxGraphBoxModel(PptxShapeModel):
-    position: PptxPositionModel
-    category_font: Optional[PptxFontModel] = None
-    value_font: Optional[PptxFontModel] = None
-    legend_font: Optional[PptxFontModel] = None
-    graph: GraphModel
-
-
 class PptxConnectorModel(PptxShapeModel):
     type: MSO_CONNECTOR_TYPE = MSO_CONNECTOR_TYPE.STRAIGHT
     position: PptxPositionModel
@@ -159,13 +149,10 @@ class PptxSlideModel(BaseModel):
         | PptxAutoShapeBoxModel
         | PptxConnectorModel
         | PptxPictureBoxModel
-        | PptxGraphBoxModel
     ]
 
 
 class PptxPresentationModel(BaseModel):
-    # theme: PresentationTheme
-    # watermark: bool
     background_color: str
     shapes: Optional[List[PptxShapeModel]] = None
     slides: List[PptxSlideModel]
diff --git a/servers/fastapi/ppt_generator/pptx_presentation_creator.py b/servers/fastapi/ppt_generator/pptx_presentation_creator.py
index 9bac33ec..aa00250e 100644
--- a/servers/fastapi/ppt_generator/pptx_presentation_creator.py
+++ b/servers/fastapi/ppt_generator/pptx_presentation_creator.py
@@ -6,26 +6,12 @@ from lxml import etree
 from pptx import Presentation
 from pptx.shapes.autoshape import Shape
 from pptx.slide import Slide
-from pptx.chart.data import ChartData, BubbleChartData
-from pptx.chart.chart import Chart
 from pptx.text.text import _Paragraph, TextFrame, Font, _Run
-from pptx.enum.chart import (
-    XL_CHART_TYPE,
-    XL_LEGEND_POSITION,
-    XL_LABEL_POSITION,
-)
 from pptx.opc.constants import RELATIONSHIP_TYPE as RT
 from lxml.etree import fromstring, tostring
 from PIL import Image
 
 from pptx.util import Pt
-from graph_processor.models import (
-    BarGraphDataModel,
-    BubbleChartDataModel,
-    GraphTypeEnum,
-    LineChartDataModel,
-    PieChartDataModel,
-)
 from pptx.dml.color import RGBColor
 from ppt_generator.models.pptx_models import (
     PptxAutoShapeBoxModel,
@@ -33,7 +19,6 @@ from ppt_generator.models.pptx_models import (
     PptxConnectorModel,
     PptxFillModel,
     PptxFontModel,
-    PptxGraphBoxModel,
     PptxParagraphModel,
     PptxPictureBoxModel,
     PptxPositionModel,
@@ -63,8 +48,6 @@ class PptxPresentationCreator:
 
         self._ppt_model = ppt_model
         self._slide_models = ppt_model.slides
-        # self._theme = ppt_model.theme
-        # self._watermark = ppt_model.watermark
 
         self._ppt = Presentation()
         self._ppt.slide_width = Pt(1280)
@@ -73,7 +56,6 @@ class PptxPresentationCreator:
         self._slide_fill = PptxFillModel(color=ppt_model.background_color)
 
     def create_ppt(self):
-        # self.set_presentation_theme()
 
         for slide_model in self._slide_models:
             # Adding global shapes to slide
@@ -120,16 +102,9 @@ class PptxPresentationCreator:
             elif model_type is PptxTextBoxModel:
                 self.add_textbox(slide, shape_model)
 
-            elif model_type is PptxGraphBoxModel:
-                self.add_graph(slide, shape_model)
-
             elif model_type is PptxConnectorModel:
                 self.add_connector(slide, shape_model)
 
-        # if self._watermark:
-        # Adding watermark
-        # self.add_picture(slide, self.get_watermark_box_model())
-
     def add_connector(self, slide: Slide, connector_model: PptxConnectorModel):
         if connector_model.thickness == 0:
             return
@@ -139,126 +114,6 @@ class PptxPresentationCreator:
         connector_shape.line.width = Pt(connector_model.thickness)
         connector_shape.line.color.rgb = RGBColor.from_string(connector_model.color)
 
-    def add_graph(self, slide: Slide, graph_box_model: PptxGraphBoxModel):
-        chart_data = None
-        chart_type = None
-        graph = graph_box_model.graph
-        match (graph.type):
-            case GraphTypeEnum.bar:
-                chart_data = self.get_bar_graph(graph.data)
-                chart_type = XL_CHART_TYPE.COLUMN_CLUSTERED
-
-            case GraphTypeEnum.scatter:
-                chart_data = self.get_scatter_graph(graph.data)
-                chart_type = XL_CHART_TYPE.XY_SCATTER
-
-            case GraphTypeEnum.bubble:
-                chart_data = self.get_bubble_graph(graph.data)
-                chart_type = XL_CHART_TYPE.BUBBLE
-
-            case GraphTypeEnum.line:
-                chart_data = self.get_line_graph(graph.data)
-                chart_type = XL_CHART_TYPE.LINE
-
-            case GraphTypeEnum.pie:
-                chart_data = self.get_pie_graph(graph.data)
-                chart_type = XL_CHART_TYPE.PIE
-
-        if chart_data:
-            chart: Chart = slide.shapes.add_chart(
-                chart_type, *graph_box_model.position.to_pt_list(), chart_data
-            ).chart
-            self.apply_graph_styles(chart, graph_box_model)
-
-    def apply_graph_styles(self, chart, graph_box_model: PptxGraphBoxModel):
-        graph = graph_box_model.graph
-
-        if graph.type in [GraphTypeEnum.pie, GraphTypeEnum.scatter]:
-            chart.has_legend = True
-            chart.legend.position = XL_LEGEND_POSITION.RIGHT
-        else:
-            chart.has_legend = False
-
-        if graph_box_model.legend_font:
-            self.apply_font(chart.font, graph_box_model.legend_font)
-
-        try:
-            category_axis = chart.category_axis
-            if graph_box_model.category_font:
-                font = category_axis.tick_labels.font
-                self.apply_font(font, graph_box_model.category_font)
-        except:
-            print("-" * 20)
-            print("Could not apply category labels style")
-
-        try:
-            value_axis = chart.value_axis
-            tick_labels = value_axis.tick_labels
-            if graph.postfix:
-                tick_labels.number_format = f'0"{graph.postfix}"'
-            if graph_box_model.value_font:
-                self.apply_font(tick_labels.font, graph_box_model.value_font)
-        except:
-            print("-" * 20)
-            print("Could not apply tick labels style")
-
-        if graph_box_model.graph.type is GraphTypeEnum.pie:
-            for plot in chart.plots:
-                try:
-                    plot.has_data_labels = True
-                    plot.data_labels.position = (
-                        XL_LABEL_POSITION.OUTSIDE_END
-                        if graph_box_model.graph.type is GraphTypeEnum.bar
-                        else XL_LABEL_POSITION.CENTER
-                    )
-                    if graph.postfix:
-                        plot.data_labels.number_format = f'0"{graph.postfix}"'
-                    if graph_box_model.value_font:
-                        self.apply_font(
-                            plot.data_labels.font,
-                            (
-                                graph_box_model.value_font
-                                if graph_box_model.graph.type is GraphTypeEnum.bar
-                                else PptxFontModel(
-                                    # size=self._theme.fonts.p2,
-                                    size=16,
-                                    bold=True,
-                                    color="ffffff",
-                                )
-                            ),
-                        )
-                except:
-                    print("-" * 20)
-                    print("Could not apply data labels style")
-
-    def get_bar_graph(self, graph: BarGraphDataModel):
-        chart_data = ChartData()
-        chart_data.categories = graph.get_categories()
-        for series in graph.series:
-            chart_data.add_series(series.get_name(), series.data)
-        return chart_data
-
-    def get_bubble_graph(self, graph: BubbleChartDataModel):
-        chart_data = BubbleChartData()
-        for each in graph.series:
-            series = chart_data.add_series(each.get_name())
-            for point in each.points:
-                series.add_data_point(*point.to_list())
-        return chart_data
-
-    def get_line_graph(self, graph: LineChartDataModel):
-        chart_data = ChartData()
-        chart_data.categories = graph.get_categories()
-        for series in graph.series:
-            chart_data.add_series(series.get_name(), series.data)
-        return chart_data
-
-    def get_pie_graph(self, graph: PieChartDataModel):
-        chart_data = ChartData()
-        chart_data.categories = graph.get_categories()
-        chart_data.add_series("", graph.series[0].data)
-        return chart_data
-
     def add_picture(self, slide: Slide, picture_model: PptxPictureBoxModel):
         image_path = picture_model.picture.path
         if (
@@ -562,17 +417,5 @@ class PptxPresentationCreator:
         font.italic = font_model.italic
         font.size = Pt(font_model.size)
 
-    # def get_watermark_box_model(self):
-    #     watermark_asset_path = f"assets/images/{'watermark_dark.png' if self._theme == PresentationTheme.dark else 'watermark.png'}"
-
-    #     return PptxPictureBoxModel(
-    #         position=PptxPositionModel(left=1120, top=685, width=140),
-    #         clip=False,
-    #         picture=PptxPictureModel(
-    #             is_network=False,
-    #             path=watermark_asset_path,
-    #         ),
-    #     )
-
     def save(self, path: str):
         self._ppt.save(path)
diff --git a/servers/fastapi/ppt_generator/slide_generator.py b/servers/fastapi/ppt_generator/slide_generator.py
index 45512a55..c3ca4b87 100644
--- a/servers/fastapi/ppt_generator/slide_generator.py
+++ b/servers/fastapi/ppt_generator/slide_generator.py
@@ -1,34 +1,24 @@
 from typing import Optional
 
-from langchain_ollama import ChatOllama
-from openai import OpenAI
+from pydantic import BaseModel
+
 from api.utils.model_utils import get_large_model, get_llm_client, get_small_model
 from ppt_config_generator.models import SlideMarkdownModel
-from ppt_generator.fix_validation_errors import get_validated_response
-
-from langchain_core.prompts import ChatPromptTemplate
 
 from ppt_generator.models.llm_models import (
     LLM_CONTENT_TYPE_MAPPING,
-    LLMSlideContentModel,
-)
-from ppt_generator.models.llm_models_with_validations import (
-    LLM_CONTENT_TYPE_WITH_VALIDATION_MAPPING,
 )
 from ppt_generator.models.other_models import SlideTypeModel
 from ppt_generator.models.slide_model import SlideModel
 
 
-def get_prompt_to_generate_slide_content(
-    title: str, outline: str, notes: Optional[str] = None
-):
+def get_prompt_to_generate_slide_content(title: str, outline: str):
     return [
         {
             "role": "system",
             "content": f"""
                 Generate structured slide based on provided title and outline, follow mentioned steps and notes and provide structured output.
 
-
                 # Steps
                 1. Analyze the outline and title.
                 2. Generate structured slide based on the outline and title.
@@ -39,10 +29,6 @@ def get_prompt_to_generate_slide_content(
                 - Slide body should not use words like "This slide", "This presentation".
                 - Rephrase the slide body to make it flow naturally.
                 - Do not use markdown formatting in slide body.
-                - **Icon query** must be a generic single word noun.
-                - **Image prompt** should be a 2-3 words phrase.
-                - Try to make paragraphs as short as possible.
-                {notes}
             """,
         },
         {
@@ -58,99 +44,93 @@ def get_prompt_to_generate_slide_content(
     ]
 
 
-prompt_template_to_edit_slide_content = ChatPromptTemplate.from_messages(
-    [
-        (
-            "system",
-            """
+def get_prompt_to_edit_slide_content(
+    prompt: str,
+    slide_data: dict,
+    theme: Optional[dict] = None,
+    language: Optional[str] = None,
+):
+    return [
+        {
+            "role": "system",
+            "content": """
                 Edit Slide data based on provided prompt, follow mentioned steps and notes and provide structured output.
 
                 # Notes
                 - Provide output in language mentioned in **Input**.
                 - The goal is to change Slide data based on the provided prompt.
                 - Do not change **Image prompts** and **Icon queries** if not asked for in prompt.
-                - Generate **Image prompts** and **Icon queries** if asked to generate or change image or icons in prompt.
-                - Ensure there are no line breaks in the JSON.
-                - Do not use special characters for highlighting.
-                {notes}
+                - Generate **Image prompts** and **Icon queries** if asked to generate or change in prompt.
 
                 **Go through all notes and steps and make sure they are followed, including mentioned constraints**
             """,
-        ),
-        (
-            "user",
-            """
-            - Prompt: {prompt}
-            - Output Language: {language}
-            - Image Prompts and Icon Queries Language: English
-            - Theme: {theme}
-            - Slide data: {slide_data}
-        """,
-        ),
+        },
+        {
+            "role": "user",
+            "content": f"""
+                - Prompt: {prompt}
+                - Output Language: {language}
+                - Image Prompts and Icon Queries Language: English
+                - Theme: {theme}
+                - Slide data: {slide_data}
+            """,
+        },
     ]
-)
 
 
-prompt_template_to_select_slide_type = ChatPromptTemplate.from_messages(
-    [
-        (
-            "system",
-            """
-            Select a Slide Type based on provided user prompt and current slide data.
+def get_prompt_to_select_slide_type(prompt: str, slide_data: dict, slide_type: int):
+    return [
+        {
+            "role": "system",
+            "content": """
+                Select a Slide Type based on provided user prompt and current slide data.
 
-            Select slide based on following slide description and make sure it matches user requirement:
-            # Slide Types (Slide Type : Slide Description)
-                - **1**: contains title, description and image.
-                - **2**: contains title and list of items.
-                - **4**: contains title and list of items with images.
-                - **5**: contains title, description and a graph.
-                - **6**: contains title, description and list of items.
-                - **7**: contains title and list of items with icons.
-                - **8**: contains title, description and list of items with icons.
-                - **9**: contains title, list of items and a graph.
+                Select slide based on following slide description and make sure it matches user requirement:
+                # Slide Types (Slide Type : Slide Description)
+                    - **1**: contains title, description and image.
+                    - **2**: contains title and list of items.
+                    - **4**: contains title and list of items with images.
+                    - **5**: contains title, description and a graph.
+                    - **6**: contains title, description and list of items.
+                    - **7**: contains title and list of items with icons.
+                    - **8**: contains title, description and list of items with icons.
+                    - **9**: contains title, list of items and a graph.
 
-            # Notes
-            - Do not select different slide type than current unless absolutely necessary as per user prompt.
+                # Notes
+                - Do not select different slide type than current unless absolutely necessary as per user prompt.
 
-            **Go through all notes and steps and make sure they are followed, including mentioned constraints**
-        """,
-        ),
-        (
-            "user",
-            """
-            - User Prompt: {prompt}
-            - Current Slide Data: {slide_data}
-            - Current Slide Type: {slide_type}
-        """,
-        ),
+                **Go through all notes and steps and make sure they are followed, including mentioned constraints**
+            """,
+        },
+        {
+            "role": "user",
+            "content": f"""
+                - User Prompt: {prompt}
+                - Current Slide Data: {slide_data}
+                - Current Slide Type: {slide_type}
+            """,
+        },
     ]
-)
 
 
 async def get_slide_content_from_type_and_outline(
     slide_type: int, outline: SlideMarkdownModel
-) -> LLMSlideContentModel:
-    response_model = LLM_CONTENT_TYPE_WITH_VALIDATION_MAPPING[slide_type]
+) -> BaseModel:
+    response_model = LLM_CONTENT_TYPE_MAPPING[slide_type]
 
     client = get_llm_client()
     model = get_small_model()
 
     response = await client.beta.chat.completions.parse(
         model=model,
+        temperature=0.2,
         messages=get_prompt_to_generate_slide_content(
             outline.title,
             outline.body,
-            response_model.get_notes(),
         ),
         response_format=response_model,
     )
-
-    with open("debug/llm_response.json", "w") as f:
-        f.write(response.choices[0].message.content)
-
-    return LLM_CONTENT_TYPE_MAPPING[slide_type].model_validate_json(
-        response.choices[0].message.content
-    )
+    return response.choices[0].message.parsed
 
 
 async def get_edited_slide_content_model(
@@ -160,28 +140,23 @@ async def get_edited_slide_content_model(
     theme: Optional[dict] = None,
     language: Optional[str] = None,
 ):
-    model = ChatOllama(model=get_large_model(), temperature=0.8)
+    client = get_llm_client()
+    model = get_large_model()
 
-    content_type_model_type = LLM_CONTENT_TYPE_WITH_VALIDATION_MAPPING[slide_type]
-    validation_model = LLM_CONTENT_TYPE_MAPPING[slide_type]
-    chain = prompt_template_to_edit_slide_content | model.with_structured_output(
-        content_type_model_type.model_json_schema()
-    )
+    content_type_model_type = LLM_CONTENT_TYPE_MAPPING[slide_type]
     slide_data = slide.content.to_llm_content().model_dump_json()
-    edited_content = await get_validated_response(
-        chain,
-        {
-            "prompt": prompt,
-            "language": language or "English",
-            "theme": theme,
-            "slide_data": slide_data,
-            "notes": "",
-        },
-        content_type_model_type,
-        validation_model,
+    response = await client.beta.chat.completions.parse(
+        model=model,
+        temperature=0.2,
+        messages=get_prompt_to_edit_slide_content(
+            prompt,
+            slide_data,
+            theme,
+            language,
+        ),
+        response_format=content_type_model_type,
     )
-
-    return edited_content.to_content()
+    return response.choices[0].message.parsed
 
 
 async def get_slide_type_from_prompt(
@@ -189,18 +164,15 @@ async def get_slide_type_from_prompt(
     slide: SlideModel,
 ) -> SlideTypeModel:
 
-    model = ChatOllama(model=get_small_model(), temperature=0.8)
+    client = get_llm_client()
+    model = get_small_model()
 
-    chain = prompt_template_to_select_slide_type | model.with_structured_output(
-        SlideTypeModel.model_json_schema()
-    )
-    slide_data = slide.content.to_llm_content().model_dump_json()
-    return await get_validated_response(
-        chain,
-        {
-            "prompt": prompt,
-            "slide_data": slide_data,
-            "slide_type": slide.type,
-        },
-        SlideTypeModel,
+    response = await client.beta.chat.completions.parse(
+        model=model,
+        temperature=0.2,
+        messages=get_prompt_to_select_slide_type(
+            prompt, slide.content.to_llm_content().model_dump_json(), slide.type
+        ),
+        response_format=SlideTypeModel,
     )
+    return response.choices[0].message.parsed