Fine-Tuning vs. RAG vs. Prompting: When to Use What
A practical decision framework for choosing the right approach based on your data, latency requirements, and budget.
Choosing the Right Approach
Should you fine-tune a model, build a RAG system, or just write better prompts? The answer depends on your specific situation — there's no universally correct choice. But there is a framework for making this decision systematically.
Each approach has different tradeoffs: cost, latency, accuracy, maintainability, and time to production. Understanding these tradeoffs helps you choose the right tool for the job and avoid expensive mistakes.
This research presents a decision framework based on our experience across dozens of LLM implementations, helping you match technique to use case.
Comparative Analysis
Time to Production
Prompting: days. RAG: weeks. Fine-tuning: months. Start with prompting, graduate to more complex approaches when you hit limits.
Knowledge Freshness
RAG wins for frequently changing information. Fine-tuning bakes in knowledge at training time — stale within months for dynamic domains.
Latency Impact
RAG adds 100-500ms for retrieval. Fine-tuned models match base model latency. For real-time applications, this difference matters.
Cost Efficiency
For high-volume use cases (10M+ requests/month), fine-tuned smaller models can be 10x cheaper than prompting large models.
Decision Framework
Start with Prompting
Always start here. If good prompts with a capable model solve your problem, you're done. Prompting is the fastest to iterate, easiest to maintain, and requires no infrastructure. Only move to RAG or fine-tuning when you hit clear limits: the model lacks knowledge it needs, or accuracy/cost requirements can't be met.
Use RAG When...
You need current information (documentation, product catalogs, recent data). The knowledge base is large (too much for context windows). Accuracy requires citing sources. Information changes frequently. You need to update knowledge without retraining. RAG is the right choice for most enterprise knowledge applications.
Use Fine-Tuning When...
You need a specific style or format consistently. Domain terminology or reasoning patterns are specialized. Latency requirements are strict. Volume justifies the investment. The knowledge is stable. Fine-tuning is powerful but expensive — make sure you actually need it.
Hybrid Approaches
Often the best solution combines techniques. Fine-tune for style and domain adaptation, use RAG for knowledge, and prompt for task specification. Each layer handles what it does best. But start simple — add complexity only when you've proven simpler approaches aren't sufficient.
Choose the Right Approach
We help you evaluate options and implement the right LLM architecture for your specific requirements.