What Is RAG (and Why Use It)?

In this post, we’ll discuss building a small retrieval-augmented agent that connects an LLM to your own documents so answers are grounded and cite sources. You’ll learn the core RAG loop (ingest → embed → store → retrieve → generate), set up a lightweight vector store, and wire a simple chat endpoint that returns evidence with each response. If you’ve made a basic API call to an LLM before, you have all the prerequisites—this guide focuses on the glue that turns raw files into a responsive, trustworthy assistant.

Connect an LLM to your own knowledge so answers are grounded and current.

• Core flow: ingest → embed → store → retrieve → generate.
• When RAG beats fine-tuning.

Project Scope

• Inputs: a few PDFs or markdown files.
• Output: a chat endpoint that cites sources.

Setup Steps

• Create embeddings; choose a vector store (Chroma/Pinecone/Weaviate).
• Chunking strategy and metadata (titles, URLs, section ids).
• Indexer script to ingest/update documents.

Query Pipeline

• Embed the question; retrieve top-k passages.
• Compose a grounded prompt with citations.
• Generate an answer; return sources.

Quality and Hardening

• Evaluate: precision/recall, answer faithfulness.
• Guardrails: max context size, harmful content filters.
• Caching and persistence; handling updates.

Next Enhancements

• Hybrid search (keyword + vector), reranking.
• Multi-document summarization; follow-up questions.
• Add tools (e.g., calculator, web fetch) for agentic behavior.

With a basic RAG agent running, you’re ready to add tools and planning for a fully agentic workflow.