RAG, also known as Retrieval Augmented Generation, is revolutionizing the optimization process of Large Language Models (LLMs). These LLMs are the backbone of intelligent chat bots and applications, aimed at providing accurate answers to user queries by referencing various knowledge sources. However, the unpredictable nature of LLMs poses challenges such as presenting information from unreliable sources and generating incorrect data due to terminology differences.
With RAG, an information retrieval component is introduced to enhance LLMs. This component retrieves relevant information from external data sources based on user input before the LLM generates a response. This collaborative approach ensures more precise outcomes.
- Creation of external data:
External data, which lies outside the LLM’s dataset, is crucial for expanding its knowledge base. This data is converted into numerical representations using encoding language models and stored in vector databases. This process creates a valuable knowledge library accessible to machines.
Retrieval of Relevant Information:
User queries are transformed into vector representations and matched against vector databases to extract pertinent information. For instance, searching for ‘RAG’ would involve converting the query into numerical form and cross-referencing data sources like research papers using vector calculations.
Augment the LLM prompt:
The RAG model enhances user input by incorporating relevant contextual data. This augmented prompt enables LLMs to generate more accurate responses to user queries.
Update the external data:
To keep external data current, the system routinely updates documents and their embedding representations through real-time or batch processing methods.
Flow of RAG
- Cost-effective Implementation: Chatbot development using Foundational Models (FMs) can be costly and time-consuming. RAG offers a more affordable alternative by eliminating the need for frequent retraining.
- Current Information: Developers can integrate RAG to provide the latest research, stats, or news directly to generative models. This enables linking LLMs to real-time data sources like social media feeds or news sites.
RAG stands out as an essential tool for grounding LLMs in up-to-date, reliable information while reducing maintenance costs. By enriching prompts with relevant data in vector form, RAG enhances the efficiency of recommendations engines and chatbots that rely on accurate information retrieval.