The Problem With Evaluating AI Developers
AI development has a demo problem.
It is genuinely easy to build an impressive-looking AI demo. GPT-4 is powerful. LangChain gives you tools. OpenAI's API is well-documented. You can get a chatbot that sounds smart, a document Q&A that returns plausible answers, or a voice agent that holds a basic conversation — in a weekend, if you know what you are doing.
The hard part is not the demo. The hard part is the system that handles 10,000 users, retrieves from a large document corpus accurately, does not hallucinate when it lacks information, stays fast under load, fails gracefully, integrates with your CRM and your billing system, and gives you the observability to know what is happening when something goes wrong.
The best AI developers know how to build the second thing. Most developers who present demos only know how to build the first.
What Good AI Development Actually Looks Like
Before you can identify the best AI developer for your project, you need to know what you are looking for. These are the skills and behaviours that separate serious AI engineers from demo builders.
Production Experience
Have they shipped AI systems that are in production, used by real people, at real scale? This is not a portfolio of concepts. It is systems that have survived contact with real usage.
Ask: show me something in production. What volume does it handle? What have you had to fix or change since it launched?
Knowing When Not to Use AI
The best AI developers push back on AI when it is the wrong tool. They have seen enough projects to know that a lot of what gets described as "needing AI" is better solved with a well-structured database query, a rules engine, or a better-designed user flow.
A developer who says "yes, we can build that with AI" to every problem is not helping you. The question you want them to ask is: "is AI actually the right approach here, and why?"
System Design Skills
LLM integration is not just API calls. It involves designing retrieval architectures, managing context windows, structuring prompts, handling output parsing, designing for failure, and building the evaluation infrastructure to know if the system is working correctly.
This is software engineering. A developer who thinks AI work is just prompting and API calls has not built real systems.
Evaluation and Observability
How do you know if your AI system is performing well? This is a harder question than it sounds. You cannot just look at whether the output seems correct — at scale, you need systematic ways to evaluate quality, detect regressions, and identify failure modes before your users do.
The best AI developers build evaluation into the system from the start. They use tools like LangSmith, Weights & Biases, or custom logging pipelines. They define metrics that matter — not just "does the user seem happy" but "what percentage of retrievals are accurate", "what is the hallucination rate", "how often does the system escalate to a human when it should not".
Clear Communication
AI projects involve a lot of uncertainty. Requirements change as you learn what the model can and cannot do. Approaches that seem right in week one turn out to be wrong in week three.
The best developers communicate clearly about this uncertainty. They surface problems early. They tell you when something is taking longer than expected and why. They do not disappear and return with something different from what was discussed.
Red Flags When Evaluating AI Developers
They only show demos. Every engagement starts with a demo. If that is also where it ends — if they cannot show you production deployments or explain what happened after the demo — be careful.
They cannot explain why they made technical choices. Why this vector database and not that one? Why this chunking strategy? Why this model? The answers reveal whether they understand the trade-offs or are just copying tutorials.
They have no escalation logic. Any AI system that handles real interactions needs a clear path to a human when the AI cannot handle something. If a developer has not thought about this, they have not built real systems.
They promise accuracy rates they cannot justify. "Our system is 95% accurate" means nothing without a clear definition of what accuracy means, a test set it was measured on, and an honest discussion of failure modes.
They have not asked about your data. The quality of an AI system is heavily determined by the quality of its data. A developer who builds your system without deeply understanding your data — its volume, its quality, its format, its gaps — will build something that works in demo conditions and fails in production.
How to Evaluate AI Developers: Practical Questions
Ask these in any developer evaluation:
- What AI systems have you shipped to production in the last 12 months? What do they do and who uses them?
- Walk me through a time an AI project you worked on failed or underperformed. What caused it and what did you do?
- How do you evaluate whether an AI system is performing well?
- How do you handle situations where the AI does not know the answer or is likely to hallucinate?
- What does your handoff and post-launch support look like?
These questions surface experience, honesty, and technical depth faster than any portfolio review.
What We Do at Woyce
We build AI agents, LLM integrations, voice AI, and AI-powered web applications. We have shipped production systems that handle real customer interactions, real document processing workflows, and real business automation.
We tell clients when AI is not the right answer. We have evaluation infrastructure. We are available after launch.
We are not the right fit for every project. We are the right fit for founders and product teams who want a development partner with genuine AI engineering depth and a track record of building things that work in production.
Talk to us about your project. We will tell you honestly what is possible and what it would take.