RAG
Retrieval Augmented Generation
Why RAG Framework is Required in the Context of LLM's Gaps
Large Language Models (LLMs) like Gemini, Llama, GPT-3 or GPT-4 are powerful tools capable of generating coherent and contextually relevant text. However, they have several limitations:
Static Knowledge Base: LLMs are trained on data available up to a certain cut-off date and cannot access new information post-training.
Memory Limitations: These models cannot remember past interactions or user-specific information across sessions.
Hallucinations: LLMs sometimes generate plausible but incorrect or nonsensical answers, as they rely solely on their training data.
Scalability Issues: Increasing the size of the model to include more information isn't always feasible due to computational constraints.
The RAG framework addresses these gaps by combining the strengths of LLMs with an external retrieval mechanism. This setup allows the model to:
Access up-to-date information in real-time.
Retrieve specific and relevant data on demand, enhancing the accuracy of the generated responses.
Reduce the need for extensive retraining whenever new information needs to be included.
Key Components Required to Design RAG-Based AI Applications
Ingestion System: A system to Ingest internal Latest Data (Documents, Images etc..) in a Database (Vector DB) convenient for the Retrieval System to extract the Latest Context information.
Indexing: The process of organizing and storing data in a way that makes retrieval efficient.
Retrieval System: A mechanism to fetch relevant documents or information from an internal database or knowledge source. This could be a search engine, a database query system, or any other form of information retrieval technology.
Semantic Searching: Formulating and executing queries to fetch relevant information based on the user's input.
Augmented Generation Model: An LLM capable of generating human-like text, such as GPT-3 or GPT-4 by taking User Prompt (Query) along with the Enhanced Context Integration
Examples to Explain the Difference of a RAG vs. Non-RAG AI Systems
Example 1: Customer Support Chatbot
Non-RAG System: A standard chatbot might provide general answers based on pre-trained data. If a customer asks about a recent policy update or a specific product issue, the chatbot might struggle to give accurate or up-to-date information since it relies solely on its training data.
RAG System: A RAG-based chatbot can fetch the latest policy documents or product manuals from the company’s database. When a customer asks about a recent update, the chatbot retrieves the relevant document, integrates it into its response, and provides accurate and current information.
Example 2: Medical Advice System
Non-RAG System: An AI trained on medical texts up to 2021 might not have information on the latest treatments or drug approvals. Patients asking about recent advancements might receive outdated advice.
RAG System: A RAG-based system can access the latest medical research papers and clinical trial results. It retrieves the most recent studies and incorporates this data into its responses, ensuring that patients receive the most current medical advice.
Example 3: Academic Research Assistant
Non-RAG System: A language model could assist in drafting papers or answering questions based on its training data but would be limited to information available up to its last training update.
RAG System: An academic assistant using the RAG framework can query databases like PubMed, Google Scholar, or specific journal repositories to retrieve the latest research articles and references. This allows it to provide cutting-edge information and more precise citations.
In summary, the RAG framework enhances LLMs by allowing them to access and utilize up-to-date, relevant information dynamically, significantly improving their accuracy and applicability in real-world scenarios.