RAG stands for Retrieval Augmented Generation. It's a powerful technique that enhances AI language models by providing them with access to external, up-to-date information before they generate responses. Traditional AI models face several key limitations: their knowledge is static and based on training data with a specific cutoff date, they can hallucinate or fabricate information, and they often lack access to specific, recent, or domain-specific data.
RAG operates through two main phases. First is the retrieval phase, where the system searches an external knowledge base to find information relevant to the user's query. This typically involves converting both the query and documents into numerical representations called embeddings, then finding documents with the most similar embeddings. Second is the generation phase, where the retrieved relevant information is provided as context to the AI model along with the original query. The AI then uses this context to generate a grounded, accurate response.
The retrieval phase involves several key steps. First, the user query is processed and converted into embeddings, which are numerical vector representations that capture the semantic meaning of the text. Next, the system searches through the document database by comparing the query embeddings with pre-computed document embeddings. Documents with the highest similarity scores are identified using mathematical distance calculations. Finally, the system ranks these documents by their similarity scores and selects the top most relevant documents to provide as context for the generation phase.
In the generation phase, the retrieved documents are provided as context to the language model along with the original user query. The AI model then processes this combined input to generate a response that is grounded in the external knowledge. This approach provides several key benefits: it delivers more accurate and relevant answers by leveraging specific information, provides access to up-to-date data beyond the model's training cutoff, significantly reduces hallucination by grounding responses in factual content, and enables integration of domain-specific knowledge that may not be present in the original training data.
To summarize what we've learned about RAG: Retrieval Augmented Generation is a powerful technique that enhances AI language models by combining external knowledge retrieval with text generation. The two-phase process first retrieves relevant information from external sources, then uses this context to generate more accurate responses. RAG provides access to current and domain-specific information while significantly reducing AI hallucination, making it an essential approach for building reliable AI applications.