Welcome to this explanation of Retrieval Augmented Generation, or RAG. Traditional Large Language Models are limited to knowledge from their training data, which can be outdated, domain-limited, and prone to hallucinations. For example, if you ask about recent events beyond their training cutoff, they cannot provide accurate information. RAG solves these problems by retrieving relevant information from external knowledge sources, allowing the model to access up-to-date or specialized information beyond its original training data.
The first step in a RAG system is indexing and preparation of the knowledge base. This involves three key processes. First, chunking breaks down large documents into smaller, manageable pieces. This is important because retrieving entire documents would be inefficient and might include irrelevant information. Second, embedding converts each text chunk into a numerical vector representation using an embedding model. These embeddings capture the semantic meaning of the text in a format that computers can process. Finally, these embeddings are stored in a searchable vector database, along with references back to the original text chunks. This database will later be used to quickly find the most relevant information when a user asks a question.
The second step in a RAG system is retrieval. When a user submits a query, the system first converts this query into an embedding vector using the same embedding model that was used during the indexing phase. This ensures that the query representation is compatible with the stored document embeddings. Next, the system searches the vector database for embeddings that are most similar to the query embedding. This is typically done using similarity metrics like cosine similarity, which measures the cosine of the angle between two vectors. The closer this value is to 1, the more similar the vectors are. Finally, the system retrieves the most relevant text chunks from the knowledge base based on these similarity scores. These chunks contain the information that is most likely to help answer the user's query.
The final two steps in a RAG system are augmentation and generation. In the augmentation step, the system combines the original user query with the retrieved text chunks to create an enhanced prompt. This prompt typically instructs the LLM to answer the user's question using only the provided context. This is crucial because it grounds the LLM's response in the retrieved information rather than its pre-trained knowledge, which might be outdated or incorrect. In the generation step, this augmented prompt is fed into the LLM, which generates a coherent and informed response based on the provided context. The LLM's language generation capabilities allow it to synthesize the information from the retrieved chunks into a natural, human-like response. This approach offers several key benefits: it provides up-to-date information beyond the LLM's training cutoff, enables access to domain-specific knowledge, significantly reduces hallucinations by grounding responses in retrieved facts, and makes responses more verifiable since they're based on specific sources.
To summarize what we've learned about Retrieval Augmented Generation systems: RAG enhances Large Language Models by connecting them to external, up-to-date knowledge sources, allowing them to provide more accurate and current information. The process involves four key steps: indexing the knowledge base by chunking documents and creating embeddings, retrieving relevant information based on query similarity, augmenting the prompt with retrieved context, and generating a response grounded in that context. This approach significantly reduces hallucinations by ensuring responses are based on retrieved facts rather than the model's internal knowledge alone. RAG systems have numerous real-world applications, including enterprise search and knowledge management, medical and legal research assistants, educational tools, and technical documentation systems. One of the key advantages of RAG is its modularity - different embedding models, retrieval methods, and LLMs can be combined to create systems optimized for specific use cases. As LLMs continue to evolve, RAG remains a powerful approach for enhancing their capabilities with external knowledge.