AI & Technical question
For a government customer, you could solve the problem with prompt engineering plus RAG on an existing model, by fine-tuning a model on customer data, or by first building a narrower workflow application around a general model. How would you decide which approach to use for v1, and what signals would make you move to a more customized model strategy later?
- Scale AI
- AI & Technical
- Hard
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What this question tests
Whether you can choose the right technical approach for a government AI use case based on real signals like data volume, latency needs, and accuracy gaps, rather than defaulting to the most sophisticated option.
How to approach it
- Start with prompt engineering plus RAG for v1 by default, since it is fastest to deploy, cheapest to iterate on, and lowest risk for a first government engagement.
- Only consider fine-tuning if RAG-based accuracy plateaus below the required bar on domain-specific language or formats that general prompting cannot capture.
- Only consider building a narrower workflow application if the core issue is not model accuracy but process complexity, like multi-step approvals or document routing the base model was never meant to handle alone.
- Watch for signals to move beyond v1: consistent accuracy gaps tied to domain vocabulary, growing volume of proprietary training data becoming available, or a workflow too complex for a single prompt-and-retrieve pattern.
- Frame the decision to the customer as staged, starting fast and cheap, with a clear technical trigger for when to invest further, rather than committing to fine-tuning up front.
What a strong answer includes
- Defaults to the lowest-cost, fastest approach and requires evidence before escalating to fine-tuning, which matches how mature AI teams actually sequence this decision.
- Distinguishes a model accuracy problem from a workflow complexity problem, since the fix for the second is an application layer, not a better model.
- Names concrete signals that would trigger moving to a more customized approach instead of leaving it as vague future work.
Common mistakes
- Jumps to fine-tuning by default because it sounds more sophisticated, ignoring cost and speed tradeoffs.
- Never gives concrete signals for when to escalate the approach, leaving the roadmap directionless.
Likely follow-up questions
- What data would you need in hand before fine-tuning becomes viable.
- How would you explain the staged approach to a customer who wants the best model immediately.
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture