Expert Q&A
Ask better questions of experts, with evidence requests and follow-ups.
- Pages
- 17
- Sources cited
- 10
- Last updated
- 26 October 2026
Press on the assumptions.
“What evidence would make you recommend that we do not fine-tune?”
Context, evidence and follow-up →02“What does parameter-efficient training save, and what does it leave unsolved?”
Context, evidence and follow-up →03“How should we balance learning the target task against forgetting other behaviour?”
Context, evidence and follow-up →04“What would convince you that our holdout is contaminated?”
Context, evidence and follow-up →05“How much can we trust an LLM judge?”
Context, evidence and follow-up →06“Which serving change most often invalidates a pilot’s apparent win?”
Context, evidence and follow-up →07“At what traffic pattern does dedicated capacity make sense?”
Context, evidence and follow-up →08“When is routing a better first experiment than adaptation?”
Context, evidence and follow-up →09“What does a usable exit package contain?”
Context, evidence and follow-up →10“What changes when this tool begins influencing a consequential decision?”
Context, evidence and follow-up →These are the desk’s proposed questions and answer framings. No participation, quotation or endorsement is implied.
Read the full research.
What should an expert interview establish?
Use an interview to test assumptions and identify evidence that would change the decision.
Who is relevant, and why?
The final row is a role specification, not an invented expert.
1. “What evidence would make you recommend that we do not fine-tune?”
Why it matters: a buyer needs an adviser whose method can produce a no-build result.
2. “What does parameter-efficient training save, and what does it leave unsolved?”
Why it matters: buyers often mistake a smaller training-memory requirement for a complete cost advantage.
3. “How should we balance learning the target task against forgetting other behaviour?”
Why it matters: a specialist that improves the main metric may become less reliable on exceptions, refusals or instructions outside the training distribution.
4. “What would convince you that our holdout is contaminated?”
Why it matters: seemingly independent rows can contain the same customer, template or derived answer.
5. “How much can we trust an LLM judge?”
Why it matters: an automated score can agree with a model’s preferred style while missing unacceptable factual or operational errors.
6. “Which serving change most often invalidates a pilot’s apparent win?”
Why it matters: model quality and latency must survive the actual runtime, concurrency and failure policy.
7. “At what traffic pattern does dedicated capacity make sense?”
Why it matters: a fixed capacity purchase can be attractive at steady load and costly when demand is intermittent.
8. “When is routing a better first experiment than adaptation?”
Why it matters: the buyer may already have a cheaper viable model for some traffic.
9. “What does a usable exit package contain?”
Why it matters: a buyer can have contractual rights but lack the technical ingredients to run elsewhere.
10. “What changes when this tool begins influencing a consequential decision?”
Why it matters: expanding a workflow can change its legal and operational risk even if the underlying model is unchanged.
How should the interview be commissioned and published?
Send the scope, audience and questions in advance.
Frequently asked questions
Short answers to the questions buyers ask.
Read →15 / REFERENCEWhat remains unproven
Evidence gaps and limits that still need resolving.
Read →16 / REFERENCEEditorial self-assessment
The research’s own quality scores and limitations.
Read →17 / REFERENCESources
Trace the section’s claims to the original sources.
Read →Useful evidence.
Visible limits.
A substantive commissioning brief with specific follow-ups and evidence requests. It deliberately contains no fabricated answers or endorsement; completed interviews are the next research step.