LLMs excel at general tasks but struggle with specialized domains. Fine-tuning enhances their performance in targeted areas, but it's complex and costly. Retrieval-Augmented Generation (RAG) offers a solution by connecting LLMs directly to knowledge bases, enabling domain-specific data retrieval without extensive retraining. Techniques like Contextual Retrieval and BM25 integration improve accuracy by situating chunks within their full context. This approach balances semantic understanding with traditional keyword search, addressing challenges like incomplete responses.
Table of contents
Anthropic’s New RAG ApproachThe Rise of LLMsWhat About Fine-Tuning Your Own LLM?The Challenges of Fine-TuningRetrieval-Augmented Generation (RAG)How RAG Systems WorkEnhancing Retrieval AccuracyRECAP BEFORE MOVING ONSort: