Comparison table

Approach Best for Cost and complexity Typical trap
Prompting Fast behavior iteration, structured response contracts Low Prompt bloat and brittle orchestration logic
RAG Knowledge freshness and factual grounding Medium Weak retrieval quality quietly wrecks answer quality
Fine-tuning Consistent formatting, style, and repetitive patterns Medium to high Training low-quality data and amplifying the noise
Custom training Domain-specific capability shifts that lighter methods cannot achieve High Underestimating evaluation and post-launch operations

Practical selection flow

  1. Define the failure in measurable terms.
  2. Try prompt and orchestration fixes first.
  3. If failure is factual, improve retrieval quality.
  4. If failure is behavioral consistency, test a fine-tune.
  5. If capability is still missing, scope custom training with strict eval gates.
Default bias: stay out of training land until evidence says you cannot hit quality targets with lighter interventions.
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