The three approaches solve different problems. Prompt engineering changes what you ask. RAG changes what the model knows. Fine-tuning changes how the model behaves. The table below shows when each wins.
Comparison at a glance
| Approach | Changes | Cost | Latency impact | Best for |
|---|---|---|---|---|
| Prompt Engineering | Instructions only | None | None (extra tokens) | Format, tone, task definition |
| RAG | Knowledge available | Low (retrieval infra) | +100–500ms | Current info, private docs, facts |
| Fine-Tuning | Model weights | High ($1K–100K+) | None (baked in) | Style, specialized behavior, speed |
When prompt engineering wins
Start here. Always.
80% of AI customization problems are solved by prompt engineering. It's free, instant, and iteratable. Before considering RAG or fine-tuning, exhaust prompt engineering: few-shot examples, explicit format requirements, chain-of-thought instructions, role definitions. Only move on when you've hit a genuine ceiling.
When RAG wins
Private or current knowledge the model doesn't have
RAG wins when the model needs access to your specific documents, databases, or recent information (post-training cutoff). Customer support with access to your knowledge base, internal policy Q&A, research assistants with access to your paper corpus — these are all RAG problems, not fine-tuning problems.
When fine-tuning wins
Behavioral style baked in at scale
Fine-tuning wins when you need consistent specialized behavior across thousands of calls — a proprietary writing style, domain-specific reasoning patterns, or structured output formats the model doesn't follow reliably via prompting alone. It's also the right call when you need to reduce prompt token cost at very high volumes (bake instructions into weights).
The production answer: combine them
Most high-performing production systems use all three: fine-tune for domain style and output format, RAG for current and private knowledge, prompt engineering for task-specific instruction per call. The combination is better than any single approach — and the order of investment should be prompt → RAG → fine-tune.
RAG and prompt orchestration on MoltBot
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