Animated +200 XP ~60 min

Advanced RAG Patterns

Four powerful patterns that take RAG from demo to production: multi-step, self-RAG, RAG+tools, and agentic RAG.

Beyond basic RAG: Simple query-retrieve-generate works for straightforward questions. But real-world queries often need multiple retrieval steps, conditional logic, external tools, or autonomous agents. These four patterns handle those cases.

Which pattern should you use?

Start here and follow the logic:

1. Is the user's question vague or uses informal language?
→ Yes: Use Multi-Step RAG (refine the query, then search again)

2. Do some queries NOT need a database search at all?
→ Yes: Use Self-RAG (let the AI decide if retrieval is even needed)

3. Does the answer require math, API calls, or live data?
→ Yes: Use RAG + Tools (combine retrieval with executable actions)

4. Does the question span multiple databases or need a multi-step plan?
→ Yes: Use Agentic RAG (let an agent plan and execute autonomously)

If none of the above apply, basic RAG (from the previous lessons) is probably all you need.

Multi-Step RAG: Like asking follow-up questions. You Google something, learn the right words, then Google again with better keywords. The AI does this automatically. Query, retrieve, refine the query based on what you found, then retrieve again. Best for vague or colloquial queries that need technical vocabulary, and multi-hop questions requiring info from different document sections.

Self-RAG: Like checking your own work. Before answering, you ask yourself "Do I actually need to look this up?" and after answering, "Am I sure about this?" The LLM decides whether it even needs to r

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