The Question Everyone Asks
After shipping two GenAI products in BFSI compliance Gov Assistant (a RAG-based Q&A tool) and Gov Co-Author (a document generator) the same question keeps coming up: "Should we use RAG or fine-tune?"
The honest answer is: it depends, and most blog posts make it sound easier than it is. The real question is not RAG vs fine-tuning. It is knowing what each one is actually good at, and where each one breaks.
When RAG Wins
Your knowledge keeps changing
Compliance documents are never still. A new circular drops, a policy gets amended, a procedure is revised. With RAG you just re-ingest the changed file. No retraining, no scheduling, no waiting. The system reflects the new info within minutes.
Gov Assistant gets small updates daily. Some files change every week. Fine-tuning at that pace would be a nightmare expensive too.
You need real citations
A fine-tuned model takes the knowledge inside its weights. When it answers, you cannot trace back to the exact paragraph that supports it. The knowledge is baked in, hidden.
RAG keeps the source visible. You can show the user the exact document, section, and paragraph the answer came from. For an auditor who needs to verify every line, that is non-negotiable.
You cannot send your data out
Fine-tuning means handing your data to a training pipeline. In regulated industries, that is often off the table. RAG keeps documents in your own vector store. The model never trains on them it just sees them at query time, inside your environment.
When Fine-Tuning Wins
You need a steady tone and shape
Gov Co-Author writes compliance documents. They have a very specific voice formal, precise, numbered sections, exact terms. No prompt I tried could give us that voice every time.
So we fine-tuned a model on more than 200 of our existing documents. The output came out clean: right format, right terminology, right level of formality without a 3,000-token prompt trying to spell it all out.
You need a fixed output shape
A compliance document is not free-form text. It has numbered clauses, cross-references in a set format ("ref. Section 4.2(a)(iii)"), tables with required headers, appendices that follow a template.
Fine-tuning on real examples teaches all this far better than a few-shot prompt. The model just knows what a "Procedure" section is supposed to look like, without us repeating it on every call.
Reasoning that prompting cannot teach
Some logic only lives inside a domain. In compliance, there is something called a regulatory hierarchy a central bank circular outranks an internal policy, which outranks a department procedure. A base model has no idea about this. Fine-tuning on examples where this hierarchy is shown teaches it to weigh sources the right way.
The Quick Checklist I Use
| Factor | Favours RAG | Favours Fine-Tuning |
|---|---|---|
| Knowledge freshness | Changes daily/weekly | Stable for months |
| Traceability needed | Must cite exact source | General knowledge is fine |
| Data sensitivity | Can't send to training | Training pipeline approved |
| Output format | Flexible / conversational | Strict structure required |
| Tone consistency | Acceptable with prompting | Must match corpus exactly |
| Reasoning complexity | Standard inference | Domain-specific logic |
| Scale of knowledge | Thousands of documents | Focused domain (<500 examples) |
What We Actually Ship: Both
In real life we do not pick one. We use both, in different parts of the same product.
Gov Assistant is pure RAG. The knowledge base is too big and changes too often for fine-tuning. Citations are required. Base GPT-4o with good retrieval handles the reasoning fine.
Gov Co-Author uses a GPT-5.1 and 5.4 model for writing (so the tone, structure, and formatting come out right), and RAG for the actual content (so the rules and references are always current). The latest model knows how to write a document and is really good at document analysis. RAG tells it what to write about.
Mistakes I Keep Seeing
"Let us fine-tune to fix the wrong answers"
If your RAG system is giving wrong answers, fine-tuning rarely fixes it. The problem is almost always retrieval the wrong chunks coming back, or the right ones ranked too low. Fix the retrieval first.
"RAG is cheaper, let's use it for everything"
RAG has hidden costs. Vector database hosting. Embedding compute on every query. Storage. Extra latency on every response. For high-traffic systems these add up fast. If your knowledge is stable and you only fine-tune once a quarter, total cost can actually be lower.
"Fine-tuning will make the model an expert"
Fine-tuning teaches patterns, not facts. Train it on 200 documents, then ask about document 201, and it will not magically know. Fine-tuning is for how to respond style, format, reasoning. For what to respond with, you still need RAG.
The 30-Second Answer
When someone asks me today, I say: RAG for knowledge, fine-tune for behavior. If you need the model to know things, use RAG. If you need it to do things in a certain way, fine-tune. If you need both, do both.
That one line has saved me from over-engineering a lot of projects.