A 2026 benchmark across 37 commercial LLMs found hallucination rates ranging from 15% to 52% even on straightforward analysis tasks. Vectara's hallucination leaderboard shows the best-engineered, properly grounded systems now sitting under 2 to 3%, which means the gap between a well-built generative AI system and a poorly built one isn't the model, it's almost entirely the developer.
That 50-point spread is exactly why a structured interview matters more for generative AI hires than for most other technical roles. Here are the ten questions worth asking before you sign a contract with a generative ai development freelancer.
1. "Show Me a RAG Project You've Shipped"
Ask for specifics: what vector database did they use, how did they choose chunk size, and how did they measure retrieval quality before shipping. A candidate who can only describe the RAG conceptually, without a specific project and specific technical decisions, likely hasn't built one in production. Generative AI Solutions for Enterprise Applications is a useful reference point for the kind of enterprise RAG use cases that a strong answer should be able to speak to concretely.
2. "How Do You Handle Hallucinations?"
Given that enterprise benchmarks show 15 to 52% hallucination rates across commercial models, a strong answer covers specific mitigation techniques: grounding responses in retrieved context, confidence thresholds that trigger a fallback response, and output validation against source documents. A weak answer treats hallucination as an unsolvable model limitation rather than an engineering problem with known mitigation patterns.
3. "What LLMs Have You Deployed to Production?"
There's a meaningful difference between having called an API in a prototype and having managed a model in production, including handling rate limits, monitoring output quality over time, and managing a provider's model version changes. LLM Integration Developer: What to Look For and Where to Find One covers exactly this distinction and the production-specific skills to screen for beyond basic API familiarity.
4. "How Do You Evaluate Outputs?"
A strong candidate describes a concrete evaluation framework: a held-out test set representative of real usage, specific metrics tied to the business outcome (not just a generic quality score), and a process for catching regressions when the underlying model or prompt changes. If the answer is "I read through the outputs and they look good," that's not an evaluation process, it's a spot check.
5. "What's Your Token Cost Optimisation Approach?"
Unmanaged token spend is one of the most common ways generative AI projects blow their budget after launch. Listen for specifics: prompt compression techniques, caching repeated queries, routing simpler requests to cheaper models, and monitoring cost per request as a tracked metric rather than an end-of-month surprise.
6. "What's Your Fine-Tuning Experience?"
Ask whether they've fine-tuned a model using LoRA or QLoRA, on what kind of dataset, and how they measured whether the fine-tune actually improved performance over the base model with good prompting alone. Many use cases don't need fine-tuning at all, so a strong candidate should also be willing to say when RAG or prompt engineering would solve the problem more cheaply.
7. "How Do You Handle Data Privacy?"
Confirm their approach to keeping your proprietary data out of a model provider's training pipeline, typically through API terms that exclude your data from training, or through self-hosted open-source models when data sensitivity requires it. A candidate who hasn't thought about this before you ask is a signal worth taking seriously, especially if your data includes customer or regulated information.
8. "What's Your Stance on IP Ownership?"
Confirm upfront, in writing, that all code, prompts, and any fine-tuned model artifacts transfer to you on payment. This should be a standard, uncontroversial term; hesitation or vagueness here is a bigger red flag than almost any technical answer in this list.
9. "What Post-Launch Support Do You Provide?"
Generative AI systems need monitoring after launch, since model providers update their models, usage patterns shift, and prompt behaviour can drift. Confirm whether post-launch monitoring is included in the original engagement or billed separately, and get specific about what "support" actually covers: bug fixes only, or ongoing tuning as real usage data comes in.
10. "How Do You Estimate Timelines?"
A candidate who quotes a firm timeline before seeing your data, your API constraints, or your existing systems is guessing. A strong answer describes a short scoping phase before committing to a timeline, and explains what could realistically extend it, such as data quality issues discovered mid-project or an integration that turns out to be more complex than initially described.
What Comes Next
As generative AI tooling matures, the technical bar for a passable demo keeps dropping, which makes structured questions like these more important, not less, since a convincing five-minute demo increasingly tells you very little about whether a system will hold up in production. The developers worth hiring are the ones who answer these ten questions with specifics from real projects, not general familiarity with the concepts. If you'd rather skip the vetting process and start from a verified track record, hire ai and ml developers with documented generative AI deployments across RAG, fine-tuning, and multi-agent systems.
Frequently Asked Questions
How they handle hallucinations is often the most revealing question, because enterprise benchmarks show commercial LLMs hallucinate 15 to 52% of the time depending on task and model, while well-engineered grounded systems achieve under 2 to 3%. A candidate's answer here reveals whether they understand hallucination as a solvable engineering problem with specific mitigation techniques, or as an unavoidable limitation they haven't seriously addressed.
Ask for specific technical details rather than accepting a general description: which vector database they used, how they chose chunk size and embedding model, and how they measured retrieval quality before shipping. A genuine practitioner can walk through these specifics fluently and unprompted; someone repeating buzzwords from a tutorial typically cannot go beyond a surface-level description when pressed for the actual decisions made.
The contract should specify that all code, prompts, and any fine-tuned model artifacts transfer to you on payment, that an NDA is signed before any proprietary data is shared, and that post-launch support terms are explicit, not assumed. Milestone-based payment tied to working deliverables, rather than a single upfront payment, protects both sides and is standard practice for well-structured generative AI engagements.
Rates for experienced generative AI freelancers typically range from $50 to $150 per hour depending on seniority and specialisation. A scoped project like a RAG-based knowledge assistant commonly costs $5,000 to $25,000, while more complex multi-agent or fine-tuned systems can run $25,000 to $60,000 or more. Monthly dedicated contracts for ongoing tuning and support typically run $8,000 to $18,000.
Unmanaged token spend is one of the most common reasons generative AI projects become unexpectedly expensive after launch, since API costs scale directly with usage volume and prompt length. A developer who proactively addresses prompt compression, caching, and model routing during the build phase can meaningfully reduce ongoing operational cost, sometimes by more than half, compared to a system built without cost optimisation in mind from the start.
Not always necessary to use, but necessary to understand as one of several tools available. A strong generative AI developer should be able to explain when fine-tuning is worth the added cost and complexity versus when RAG or better prompt engineering would solve the same problem more cheaply. A candidate who defaults to fine-tuning for every use case, without weighing the alternatives, may be optimising for a technique they know rather than the outcome you actually need.
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