--- title: "RAG vs fine-tuning vs agents | Lookup, weights, worker | Len P. van der Hof" description: RAG looks up a named passage. Fine-tuning changes weights for a stable skill. An agent holds a goal and uses tools under a charter. They are three jobs. Mixing them is how a chatbot inherits an API key and still cannot open the source. image: "https://lenvanderhof.com/media/generated/blog-hero-rag-vs-finetune-vs-agents-v1.86b37f8dd3d7.wide.webp" --- [AI Systems](https://lenvanderhof.com/en/blog/category/ai-systems/) Research guide # RAG vs fine-tuning vs agents: three jobs, not a ladder A vector store is not a trained model. A trained model is not a worker. A worker without a ceiling is a demo. Len P. van der HofPublished 3 September 20262 min read Lookup. Weights. Worker. Three benches. Direct answer RAG, fine-tuning, and agents are three jobs. RAG, retrieval-augmented generation, looks up a named passage in a corpus you gave it, then writes. Fine-tuning changes model weights so a stable skill fires without fetching a document. An AI agent holds a goal, uses tools, and continues across steps under a written charter. They are not a ladder of sophistication. Fine-tuning does not make the model know Tuesday's PDF. RAG does not give the model a ceiling. An agent that cannot open a source still needs retrieval or a refuse. What is RAG owns the method. What is an AI agent owns the worker. This page is the comparison. The RAG Engineer is live. Fine-tuning win-rates are not this page. ## Key takeaways - RAG = named passage. Fine-tuning = weights. Agent = goal plus tools plus a ceiling. - Not a ladder. Pick the job that failed, not the slide that is fashionable. - Fine-tuning is not a source of truth. RAG is not a charter. - This page is the three-way. The definition pages stay next door. A chatbot can inherit an API key and still be unable to open the source behind its sentence. That failure is why **RAG, fine-tuning, and agents** must be separated by job. **RAG vs fine-tuning vs agents.** Three jobs. Not a maturity model. [What is RAG?](https://lenvanderhof.com/en/blog/what-is-rag/) owns the lookup. [What is an AI agent?](https://lenvanderhof.com/en/blog/what-is-an-ai-agent/) owns the worker. This page is the comparison. ## Three artefacts **RAG.** Retrieval-augmented generation. Look up a named passage in a corpus you gave the system, then write. The artefact is an openable locator: file, page, chunk, date. If you cannot open it, you do not have RAG. You have extra text in a prompt. [Evaluate the reasoning, not the fluency](https://lenvanderhof.com/en/blog/evaluate-reasoning-not-fluency/) is the same test. **Fine-tuning.** A weight change so a stable skill fires without fetching a document: a tone, a format, a classifier that does not move every Tuesday. The artefact is a trained checkpoint with an eval that still holds on held-out cases. Fine-tuning does not make the model know last week’s contract. A changed PDF is a retrieval problem. Treating fine-tuning as a filing cabinet is how stale clauses survive a model upgrade. **Agent.** A system that holds a goal, uses tools, and continues across steps under a charter. The artefact is seven written fields and a named person who can fail the work. [Using AI agents effectively](https://lenvanderhof.com/en/blog/using-ai-agents-effectively/) is the operating loop. An agent may *call* retrieval as a tool. That does not turn the agent into RAG, and it does not replace the lookup test. ## Not a ladder Doing all three is allowed. Doing them in a fashionable order is not a strategy. Fail the lookup: you need RAG, or a refuse, not a bigger model. Fail a stable format that never needs a document: fine-tuning can be the job. A weekly handbook is not that job. Fail the ceiling: you need a charter, not another index. A vector store is furniture. GRAIN, on this site, is the inspectable retrieval loop. Fine-tuning is not GRAIN. An agent without Inspect still answers from the closest wrong paragraph. ## A proposal you can fail If a vendor sells one retainer for all three, ask which artefact you will hold on Friday: a locator you can open, a checkpoint with an eval, or a charter with a named reviewer. Missing all three, you bought a demo. [The RAG Engineer](https://lenvanderhof.com/books/the-rag-engineer/) is live. It does not turn fine-tuning into a source of truth. Write which job failed. Then stop stacking rungs. ## Terminology - [RAG](https://lenvanderhof.com/glossary/rag/) - [AI agent](https://lenvanderhof.com/glossary/ai-agent/) ## Sources 1. [What is RAG?](https://lenvanderhof.com/en/blog/what-is-rag/) 2. [What is an AI agent?](https://lenvanderhof.com/en/blog/what-is-an-ai-agent/) 3. [Using AI agents effectively](https://lenvanderhof.com/en/blog/using-ai-agents-effectively/) 4. [Evaluate the reasoning, not the fluency](https://lenvanderhof.com/en/blog/evaluate-reasoning-not-fluency/) 5. [The RAG Engineer](https://lenvanderhof.com/books/the-rag-engineer/) ## Further reading - [What is RAG?](https://lenvanderhof.com/en/blog/what-is-rag/) - [What is an AI agent?](https://lenvanderhof.com/en/blog/what-is-an-ai-agent/) - [The RAG Engineer](https://lenvanderhof.com/books/the-rag-engineer/) About the author ## [Len P. van der Hof](https://lenvanderhof.com/en/authors/len-p-van-der-hof/) Entrepreneur, AI Innovator and Venture Builder Len P. van der Hof builds practical AI systems, digital ventures and evidence-informed tools for founders. ```json { "@context": "https://schema.org", "@graph": [ { "@type": "Person", "@id": "https://lenvanderhof.com/#person", "name": "Len P. van der Hof", "alternateName": [ "Len van der Hof", "L.P. van der Hof", "Leendert Pieter van der Hof" ], "honorificSuffix": "MSc", "url": "https://lenvanderhof.com/", "image": [ "https://lenvanderhof.com/photos/len-portrait-1.jpg", "https://lenvanderhof.com/photos/len-portrait-2.jpg", 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