CTP Decision Summary Assistant

August 29, 2024

I'm always experimenting on better use-cases for the RAG LLM framework so I extracted ~2500 CTP decision summary documents (basically the technical project lead memos) from the Searchable Tobacco Products Database. I focused on the EX REQ, SE and PMTA documents. The goal is to create a functional sentiment analysis tool for analyzing the TPLs in aggregate and also to be able to create templates for future applications in a standardized manner once templates and data sets are available.

Having a Retrieval-Augmented Generation (RAG) Language Model (LLM) for sentiment analysis among documents can provide substantial value in several ways:

1. Enhanced Accuracy: RAG models combine the strengths of retrieval-based systems and generative models. They can access a vast repository of documents to retrieve relevant information, leading to more accurate sentiment analysis by considering context and nuances present in similar documents.

2. Contextual Understanding: By retrieving contextually relevant documents, RAG models can better understand the sentiment of a specific document. This is particularly useful when the sentiment is subtle or requires domain-specific knowledge.

3. Scalability: RAG models can efficiently process large volumes of documents, making them suitable for organizations that need to analyze sentiment across extensive datasets, such as customer reviews, social media posts, or internal reports.

4. Adaptability: These models can be fine-tuned to specific domains or industries, allowing them to provide more targeted sentiment analysis that aligns with the unique language and sentiment expressions of a particular field.

5. Real-Time Insights: RAG models can provide real-time sentiment insights by quickly retrieving and analyzing relevant documents, enabling businesses to make timely decisions based on current sentiment trends.

6. Comprehensive Analysis: By leveraging a wide range of documents, RAG models can offer a more comprehensive sentiment analysis, taking into account various perspectives and reducing bias that might occur with a smaller dataset.

🔗 CTP Decision Summary Assistant

Check it out and let me know what you think. I also have the decision summary document training set available.