When to Use RAG vs Fine-Tuning

The Question That Keeps Coming Up

"Should we use RAG or fine-tune a model?"

I've been asked this dozens of times. The answer is always: "It depends." But that's not helpful, so here's the framework I actually use.

Quick Definitions

RAG (Retrieval-Augmented Generation): Give the base model relevant documents at query time. The model uses those documents to answer.

Query → Retrieve relevant docs → Add docs to prompt → Generate answer

Fine-tuning: Train the model on your data to internalize knowledge and style. The model "remembers" what it learned.

Training data → Train model → Use modified model → Generate answer

The Decision Matrix

When RAG is the Clear Choice

1. Your Data Changes Frequently

If your knowledge base updates daily, weekly, or monthly, RAG is almost always better.

Example: Product catalog, pricing, documentation, policies.

Fine-tuning would require retraining every time data changes. With RAG, you just re-index the new documents.

Fine-tuning: Update data → Retrain ($$$, days) → Deploy
RAG: Update data → Re-index (minutes) → Done

2. You Need Source Attribution

RAG naturally provides sources—the retrieved documents are the citations.

Example: Legal research, medical information, any high-stakes factual query.

"According to [Policy Document v3.2, Section 4.1], employees are entitled to..."

Fine-tuning bakes knowledge into weights—there's no trace of where it came from.

3. You Have Sensitive Data

With RAG, data stays in your database. The LLM only sees relevant chunks at query time.

Example: HR records, customer data, proprietary information.

Fine-tuning embeds data in model weights. Those weights could theoretically be extracted or leaked.

4. You Need Results Fast

RAG can be production-ready in days. Fine-tuning takes weeks.

RAG timeline:
Day 1: Index documents
Day 2: Build retrieval pipeline
Day 3: Tune prompts
Day 4: Deploy

Fine-tuning timeline:
Week 1: Prepare training data
Week 2: Training runs
Week 3: Evaluation and iteration
Week 4: Deploy and monitor

If you need something working next week, RAG.

When Fine-Tuning is the Clear Choice

1. You're Changing Model Behavior

RAG provides information. Fine-tuning changes how the model thinks, reasons, and responds.

Example: Customer support tone, domain-specific reasoning patterns, consistent formatting.

If you want the model to respond in your company's voice for every query—not just when you have relevant documents—fine-tuning is the path.

2. Query Volume is Very High

RAG adds per-query costs: embedding, retrieval, extra tokens in context.

Rough math:

• RAG overhead: ~$0.01 per query (embedding + retrieval + longer context)
• Fine-tuned model: ~$0.002 per query (no overhead)

At 1 million queries/month:

• RAG: ~$10,000 overhead
• Fine-tuned: ~$0 overhead (model cost only)

Above a certain volume, fine-tuning's upfront cost pays off.

3. You Need Complex Reasoning

RAG retrieves facts. Fine-tuning teaches reasoning patterns.

Example: "Given these symptoms, what's the likely diagnosis?"

A RAG system can retrieve relevant medical literature. A fine-tuned model learns diagnostic reasoning patterns from training on case studies.

4. Your Knowledge is Stable

If data doesn't change, there's no downside to baking it into the model.

Example: Historical facts, established scientific principles, regulatory frameworks.

The Hybrid Approach

Often, the answer is both.

Fine-tune for:

• Domain vocabulary and concepts
• Response style and format
• Reasoning patterns

Use RAG for:

• Specific, current facts
• Citable information
• User-specific context

Example: Legal AI Assistant

• Fine-tuned on: Legal reasoning patterns, citation formats, professional language
• RAG for: Specific case law, current regulations, client documents

The fine-tuned model knows how to think like a lawyer. RAG provides the specific facts for each query.

The Practical Decision Tree

┌─────────────────────────────────────────┐
│ Does your data change regularly?        │
│                                         │
│    YES → RAG                            │
│    NO  → Continue ↓                     │
└─────────────────────────────────────────┘
                    │
                    ▼
┌─────────────────────────────────────────┐
│ Do you need source attribution?         │
│                                         │
│    YES → RAG                            │
│    NO  → Continue ↓                     │
└─────────────────────────────────────────┘
                    │
                    ▼
┌─────────────────────────────────────────┐
│ Are you mainly changing behavior/style? │
│                                         │
│    YES → Fine-tuning                    │
│    NO  → Continue ↓                     │
└─────────────────────────────────────────┘
                    │
                    ▼
┌─────────────────────────────────────────┐
│ Is query volume very high (>100K/mo)?   │
│                                         │
│    YES → Fine-tuning (or hybrid)        │
│    NO  → RAG                            │
└─────────────────────────────────────────┘

Common Mistakes

Mistake 1: Fine-Tuning for Facts

"We'll fine-tune the model on our documentation."

This sounds reasonable but usually fails. Fine-tuning is bad at memorizing specific facts. The model "kind of" learns the content but hallucinates details.

Better: Use RAG for facts. Fine-tune for behavior.

Mistake 2: RAG for Style Changes

"We'll use RAG to make the model respond in our brand voice."

RAG provides information, not personality. You'd need to include style examples in every query, wasting tokens.

Better: Fine-tune for consistent style changes.

Mistake 3: Not Considering Maintenance

RAG requires ongoing pipeline maintenance: chunking, indexing, retrieval tuning.

Fine-tuning requires periodic retraining and model management.

Neither is "set and forget." Choose based on which maintenance burden your team can handle.

Mistake 4: Overcomplicating the Choice

For most applications, RAG is the right first choice:

• Faster to implement
• Easier to debug (you can see what was retrieved)
• More transparent
• Easier to update

Start with RAG. Graduate to fine-tuning or hybrid when you have evidence you need it.

Cost Comparison

RAG Costs (Monthly)

Fine-Tuning Costs (One-Time + Monthly)

Break-even depends on query volume. Roughly: if you're spending >$1000/month on RAG overhead and data is stable, consider fine-tuning.

Real Examples

Example 1: Customer Support Bot

Requirement: Answer questions about products and policies. Data: 500 product pages, 50 policy documents, updated monthly. Volume: 10,000 queries/month.

Decision: RAG

Why: Data changes monthly, need citations, volume isn't high enough to justify fine-tuning.

Example 2: Code Assistant

Requirement: Help developers write code in a proprietary framework. Data: Framework documentation (stable), coding patterns, best practices. Volume: 100,000 queries/month from 500 developers.

Decision: Hybrid (fine-tuned model + RAG for docs)

Why: High volume, need to internalize coding patterns, but still need current docs.

Example 3: Medical Scribe

Requirement: Generate clinical notes in specific format. Data: 10,000 example notes, standardized templates. Volume: 50,000 notes/month.

Decision: Fine-tuning

Why: Behavior change (formatting, clinical language), stable templates, high volume, no need for external retrieval.

Conclusion

The choice isn't RAG vs. fine-tuning—it's understanding what each approach is good at:

• RAG: Current facts, citations, privacy, speed
• Fine-tuning: Behavior change, learned reasoning, high volume, static knowledge

When in doubt, start with RAG. It's faster, more debuggable, and keeps data fresher.

Add fine-tuning when you have evidence that behavior changes need to be internalized or that retrieval overhead is a real cost problem.

What approach do you use? Have you tried hybrid solutions?