Standard RAG: The Foundation of Retrieval-Augmented Generation

TL;DR

Standard RAG pairs an LLM with a retriever (often a vector DB) to ground answers in external documents, reducing hallucinations and enabling fresh, domain-specific knowledge.

What Is Standard RAG?

A retrieve-then-generate pipeline: split text, embed chunks, store in a vector index, perform similarity (or hybrid) search for a query, then pass retrieved context + question to the LLM for an answer.

When to Use Standard RAG

• Open-domain QA and document search
• Customer support chatbots grounded in KBs
• Research assistants requiring up-to-date facts
• Legal/medical Q&A where citations are needed

Building Standard RAG in N8N

• Preprocess: split documents into chunks (Function/Built-in nodes)
• Embed chunks and store in a vector DB (e.g., Chroma, Pinecone)
• On query: compute embedding and run top-K similarity search
• Optionally fuse with BM25/hybrid ranking or rerank
• Concatenate top results into prompt and call LLM
• Return answer with citations; log for evaluation

Strengths & Weaknesses

Strengths: accesses fresh domain data without training, reduces hallucinations, simple and widely applicable. Weaknesses: hinges on retrieval quality and index freshness; too many/irrelevant chunks can harm results.

Implementation Patterns

• Hybrid retrieval (Embeddings + BM25) and Reciprocal Rank Fusion
• Reranking top candidates with an LLM or learned reranker
• Context compression to fit token limits
• Citation formatting and logging for audits

Metrics to Track

• Retrieval precision/recall and hit rate
• Answer accuracy (e.g., F1 on QA sets)
• Factuality/hallucination rate
• End-to-end latency and token cost