RAG Frameworks: Understanding Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) remains a primary method for grounding large language models in private or domain-specific data without the need for retraining. The RAG framework typically functions through a three-tier process: a retriever that sources information from a large corpus, a ranker that prioritizes the most pertinent results, and a Large Language Model (LLM) that synthesizes the final response.

The standard RAG pipeline consists of ingestion, retrieval, and generation. During ingestion, documents are chunked, embedded, and indexed. Retrieval involves matching a user query against that index, and generation produces the final answer based on the retrieved results.

While semantic caching can improve latency and cost by serving previous answers to similar queries, it is noted that such caching only stores answers rather than building a persistent model of domain understanding. Consequently, when a query falls outside the similarity threshold, the system must restart the entire reasoning process from scratch.