Building RAG Systems: A Complete Guide
Retrieval-Augmented Generation (RAG) systems are a crucial technique for enhancing Large Language Models (LLMs) by allowing them to access and utilize external, up-to-date information. RAG addresses LLM limitations such as knowledge cutoffs and context window limits by retrieving relevant data before generating a response. This approach is distinct from fine-tuning, which modifies the model's behavior rather than its knowledge base. Building a RAG system involves two main pipelines: an ingestion pipeline for preparing and storing data, and a retrieval pipeline that fetches context for each user query. AI
IMPACT Enables LLMs to provide more accurate, up-to-date, and domain-specific answers by integrating external knowledge bases.