Added flow diagram

end-to-end-use-cases/benchmarks/evals_synthetic_data/README.md

@@ -6,11 +6,17 @@ The notebook also shows how one could accurately measure hallucinations without
 
 ## Overall idea
 
-Let's assume we have a use case for generating a report based on a given context, which is a pretty common use case with LLM. Both the context and the report have a lot of factual information and we want to make sure the generated report is not hallucinating.
+Let's assume we have a use case for generating a summarization report based on a given context, which is a pretty common use case with LLM. Both the context and the report have a lot of factual information and we want to make sure the generated report is not hallucinating.
 
-Since its not trivial to find an open source dataset for this, the idea is to take synthetic tabular data and then use Llama to generate a story(context) for every row of the tabular data. Finally we ask Llama to summarize the generated context as a report in a specfic format.
+Since its not trivial to find an open source dataset for this, the idea is to take synthetic tabular data and then use Llama to generate a story(context) for every row of the tabular data using Prompt Engineering. Then we ask Llama to summarize the generated context as a report in a specfic format using Prompt Engineering. Finally we check the factual accuracy of the generated report using Llama by converting this into a QA task using the tabular data as the groud truth.
 
-To generate synthetic data for this approach, we use an open source tool like [Synthetic Data Vault](https://github.com/sdv-dev/SDV)
+To generate synthetic data for this approach, we use an open source tool like [Synthetic Data Vault(SDV)](https://github.com/sdv-dev/SDV)
+
+The overall workflow is shown in the below diagram
+
+![Workflow](./Workflow_Diagram.png)
+
+- 
 
 ## Example Context & Report
 

end-to-end-use-cases/benchmarks/evals_synthetic_data/evals_with_synthetic_data.ipynb

@@ -18,11 +18,15 @@
    "source": [
     "## Overall idea\n",
     "\n",
-    "Let's assume we have a use case for generating a report based on a given context, which is a pretty common use case with LLM. Both the context and the report have a lot of factual information and we want to make sure the generated report is not halucinating.\n",
+    "Let's assume we have a use case for generating a summarization report based on a given context, which is a pretty common use case with LLM. Both the context and the report have a lot of factual information and we want to make sure the generated report is not hallucinating.\n",
     "\n",
-    "Since I could not find an appropriate dataset for this, the idea is to take synthetic tabular data, use Llama to generate a story(context) for every row of the tabular data. Then we ask Llama to summarize the generated context as a report in a specfic format.\n",
+    "Since its not trivial to find an open source dataset for this, the idea is to take synthetic tabular data and then use Llama to generate a story(context) for every row of the tabular data using Prompt Engineering. Then we ask Llama to summarize the generated context as a report in a specfic format using Prompt Engineering. Finally we check the factual accuracy of the generated report using Llama by converting this into a QA task using the tabular data as the groud truth.\n",
     "\n",
-    "To generate synthetic data for this approach, we use an open source tool like [Synthetic Data Vault](https://github.com/sdv-dev/SDV)"
+    "To generate synthetic data for this approach, we use an open source tool like [Synthetic Data Vault](https://github.com/sdv-dev/SDV)\n",
+    "\n",
+    "The overall workflow is shown in the below diagram\n",
+    "\n",
+    "![Workflow](./Workflow_Diagram.png)"
    ]
   },
   {