LLMs, GenAI, RAG, agents, evaluation · insight-0541
Fine-tune for consistent behaviour that long prompts cannot hold
- Evidence type
- practitioner heuristic
- Difficulty
- medium
- Roles
- Applied AI, ML engineer
- Industries
- aviation
Summary: situation, action, reason, boundary
Situation
A prompt for drafting delay notices needs a long style guide and many examples, yet tone and structure still drift across thousands of daily messages.
Action
Collect a few hundred reviewed input-output pairs that show the target format and tone, fine-tune (often with PEFT), and compare against the long prompt on consistency metrics, quality, latency and prompt length.
Reason
Vendor guidance positions fine-tuning for consistent behaviour such as format and tone, which can also shorten prompts.
Boundary
High-volume, stable-format generation and classification tasks.
Symptom or risk
Prompts grow, cost rises, and inconsistency remains despite every added instruction.
Exceptions
When the format changes often, prompting keeps you flexible.
Trade-offs
Training data curation, retraining when the base model changes, and a new artefact to monitor.
Pitfalls
Including inconsistent historical examples that teach the drift you wanted to remove.
Lessons and concepts
Claims and their sources
OpenAI's accuracy-optimisation guide describes fine-tuning as a way to make model behaviour such as format and tone more consistent.