Topic
Agentic RAG
14 posts on agentic rag — part of ai agents & automation on the n4n AI blog.
When agentic RAG is overkill for your use case
A practical analysis of when to use agentic RAG versus simpler retrieval pipelines, with code examples and tradeoffs for engineers building LLM apps.
Vector search vs agentic retrieval: what changes
A practical comparison of vector search vs agentic retrieval across capabilities, cost, latency, ergonomics, and limits for engineers building RAG.
Multi-hop retrieval with agentic RAG agents
Practical guide to building multi-hop retrieval agentic RAG systems: state design, planner loops, tool execution, and pitfalls for production LLM apps.
How to reduce hallucinations in agentic RAG systems
Practical steps to reduce hallucinations agentic RAG systems using citation constraints, output validation, critic loops, and resilient model routing.
How to evaluate an agentic RAG pipeline
A practical guide to building an evaluation harness for agentic RAG pipelines: measure trajectories, retrieval quality, and judge scoring with runnable code.
How to add query rewriting to an agentic RAG system
Add query rewriting to an agentic RAG system with this hands-on guide: implement a rewriter, parallelize retrieval, and verify retrieval gains.
How much does agentic RAG cost per query
Analyze the real token and model drivers behind agentic RAG cost per query, with concrete examples and engineering controls to keep expenses predictable.
How agentic RAG decides when to retrieve
Engineer's guide to agentic RAG retrieval decision: building state machines, tool use, confidence scoring, and routing to cut unnecessary fetches.
What is agentic RAG and how is it different from RAG
Agentic RAG adds autonomous retrieval, reasoning, and tool use to traditional RAG. Learn how it works, why it differs, and where it breaks down.
How to build an agentic RAG pipeline step by step
Step-by-step tutorial to build agentic RAG pipeline with routing, retrieval, and tool use. Runnable Python code and expected outputs included.
Agentic RAG with self-correction: how it works
A practical guide to building self-correcting agentic RAG systems: architecture, retrieval loops, verification, and common failure modes engineers hit.
Agentic RAG with LangGraph: a hands-on walkthrough
Step-by-step agentic RAG LangGraph tutorial: implement a retrieval agent with tool routing, reflection, and fallback using Python and LangChain.
Agentic RAG vs traditional RAG: a comparison
Head-to-head comparison of agentic RAG vs traditional RAG across capabilities, cost, latency, ergonomics, ecosystem, and limits, with verdict.
Agentic RAG architectures: router, planner, and critic
Explore three core agentic RAG architecture patterns—router, planner, and critic—with concrete code and engineering tradeoffs for production LLM systems.
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