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Hiring an AI developer: 12 questions to ask before you sign

How to hire a freelance AI developer in 2026: the interview questions that reveal real LLM, RAG and evaluation experience, plus budgets and red flags.

By Daniel Okafor

AI development is the fastest-growing category on Taskora, and also the hardest to hire for. The skill is new, the vocabulary is easy to imitate, and a convincing demo can be built in an afternoon. What separates a demo from a production feature — reliability, cost control, evaluation, security — is exactly what a short interview often misses.

This guide gives you twelve questions that reveal real experience, what good answers sound like, and the budget ranges we see in 2026.

First, be clear about what you need

The 12 questions

1. “Tell me about an AI feature you shipped that real users rely on.”

Look for specifics: the users, the volume, what it replaced, what went wrong after launch. A candidate who only has hackathon projects can still be good, but should say so.

2. “How did you know it was working well enough?”

This is the most important question. Strong candidates describe an evaluation set — dozens to hundreds of real examples with expected outcomes — and how they measured quality before and after changes. Weak answers rely on “it looked good when we tested it”.

3. “What did it cost per request, and how did you reduce it?”

Experienced developers know their cost per request or per user, and have used techniques like caching, smaller models for simple steps, prompt compression or batching.

4. “How do you handle wrong or made-up answers?”

Good answers include grounding answers in retrieved sources, showing citations, allowing the model to say “I don’t know”, confidence thresholds, and human review for high-stakes outputs.

5. “How would you approach our data?”

Share a real sample. Listen for questions about format, size, update frequency, permissions and quality. A candidate who proposes an architecture without asking about the data is guessing.

6. “How do you protect against prompt injection and data leaks?”

Expect discussion of separating instructions from user content, limiting what tools and data the model can access, respecting user permissions in retrieval, and never letting model output trigger sensitive actions without checks.

7. “What happens when the model provider changes or is down?”

Production systems need timeouts, retries, fallbacks and a way to pin or test model versions. Bonus points for abstracting the provider so you can switch models later.

8. “Which parts would you not use AI for?”

Great AI developers are sceptical. They will tell you which steps are better handled by ordinary code, search or rules — often the majority of the system.

9. “How will you log and monitor it after launch?”

Look for plans to log inputs and outputs (with privacy in mind), track quality signals such as user feedback, and review failures regularly.

10. “What will you hand over at the end?”

Code, the evaluation set, documentation of prompts and configuration, cost estimates at different volumes, and a runbook for common issues.

11. “How would you split this into milestones?”

A good plan starts with a small proof of value on real data and an evaluation baseline, before investing in UI and integration.

12. “What would make this project fail?”

Honest candidates name risks: poor source data, unclear success criteria, unrealistic accuracy expectations, legal constraints. That honesty is worth more than optimism.

Ask every shortlisted candidate the same questions and write down their answers. Differences that are invisible in a conversation become obvious side by side.

Budgets in 2026

Remember running costs. Model usage for a support assistant answering a few thousand questions a month often costs tens to a few hundred dollars; high-volume document processing can cost much more. Ask for an estimate at your expected volume before you build.

What a strong proposal looks like

Before any interview, the proposal itself tells you a lot. Strong AI developers restate your use case in their own words, ask for sample data, propose a small proof of value with a measurable target, and give a rough estimate of running costs. They mention evaluation without being prompted. Weak proposals list model names and frameworks, promise “state-of-the-art accuracy”, and skip straight to a full build.

It also helps to look at the portfolio closely. A case study that shows a before-and-after quality metric, a cost per request and a description of what went wrong in the first version is worth more than a polished chatbot demo video. If a candidate has worked under a non-disclosure agreement, ask them to describe the system architecture and the evaluation approach instead — experienced engineers can do that without revealing anything confidential.

Red flags

Structure the engagement

Start with a fixed-price discovery and proof-of-value milestone. Define success as a measurable target on the evaluation set, such as “85% of test questions answered correctly with a cited source”. Fund the build milestones only if the proof of value meets it. This protects your budget and gives the developer a clear, fair target.

The best AI developers will welcome this structure. It is how they would run the project anyway.