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.
This distinction matters more now than it did two years ago. The barrier to producing a plausible-looking AI prototype has dropped to near zero. Anyone with a few days of Python experience and access to OpenAI's documentation can show you something impressive in a Zoom call. What they cannot show you is three months of that system running in production without blowing up — because they have never done it.
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?
Production experience reveals things that prototyping never exposes. A retrieval system that works fine with 500 documents starts returning irrelevant chunks when you load 50,000. A chatbot that handles polite test queries starts hallucinating when real users ask ambiguous questions, paste in legal jargon, or ask in languages the team never tested. A voice agent that performs in a quiet office fails when a user calls from a car.
A 12-person law firm using an AI document review tool needs the system to handle contract PDFs with complex formatting — tables, redlines, embedded footnotes — not just clean Word documents. The developer who has built only demos has never encountered that problem. The one who has shipped production systems has already solved it.
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?"
For example, a growing e-commerce company wanted to build an AI agent to handle returns. The real problem was that their returns policy had 14 exceptions that nobody had documented consistently. The right answer was to document the policy, build a simple decision tree, and present it clearly — not to train an LLM to guess at ambiguous rules. An honest AI developer would tell you that. A developer trying to win the project will not.
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.
The specific skills matter: understanding when to use dense versus sparse retrieval, how to structure chunks to preserve context across boundaries, how to manage token costs at scale, how to handle rate limits gracefully, when to use streaming versus synchronous responses. None of this appears in tutorials. It comes from building systems that break and fixing them.
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".
A healthcare software company running an AI assistant for patient intake needs to know, at the query level, whether the system is misinterpreting symptom descriptions. That requires structured logging from day one — not retrofitted monitoring added after something goes wrong.
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.
They quote a fixed price before understanding the scope. AI projects have more unknowns than standard software projects. A developer who gives you a firm fixed price in week one either has more experience than they need for your project (in which case you are overpaying for certainty) or is guessing and will revise that price later. Ask instead how they scope projects and what happens when assumptions change.
Off-the-Shelf AI vs. Custom-Built AI: What to Expect
Before you even evaluate developers, it helps to know whether you need custom development at all. Many businesses jump straight to hiring a developer when a configured off-the-shelf tool would serve them better — and some hire a tool when their problem actually requires custom engineering.
| Off-the-Shelf AI Tool | Custom-Built AI System | |
|---|---|---|
| Time to deploy | Days to weeks | 6–16 weeks typical |
| Upfront cost | Low ($0–$500/month) | $15,000–$150,000+ depending on complexity |
| Ongoing cost | Predictable subscription | Hosting + LLM API costs + maintenance |
| Fits your workflow | Partial — you adapt to the tool | High — built around your data and process |
| Data stays private | Depends on vendor terms | Fully under your control |
| Handles edge cases | Generic fallbacks | Designed for your specific failures |
| Scales with volume | Usually — check pricing tiers | Designed for your target load |
| Requires technical staff | No | Yes, or a retained developer |
The honest answer: if your use case is common (meeting transcription, customer support ticketing, document summarisation for standard formats), try a configured off-the-shelf product first. If your use case involves proprietary data, multi-step workflows, or integration with internal systems that no vendor supports, you need custom development.
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.
A few additional questions worth asking if budget and timeline are in play: How do you scope projects with significant unknowns? What has caused projects you have worked on to run over schedule, and how did you handle that? Do you have existing clients we can speak to about a system currently in production?
What to Expect in Practice
Most AI development projects go through a predictable shape, even when the specifics differ.
The first two to four weeks are discovery: understanding your data, your existing systems, your users, and the exact problem to solve. This is not padding. It is where the assumptions that will shape the whole project get established. Projects that skip this phase — where a developer takes a brief and starts building immediately — tend to produce things that do not fit.
Weeks four to ten are typically a working prototype followed by iteration. This is where most of the uncertainty lives. The model behaves differently than expected on your real data. The retrieval approach that worked in testing fails on documents formatted a certain way. The response latency is higher than required. These are normal problems; what matters is how quickly and transparently they are surfaced and resolved.
From week ten onward, the work shifts to hardening: performance under load, failure handling, integration with live systems, monitoring, and the operational runbook that tells your team what to do when something breaks.
Common mistakes at each phase:
In discovery, the mistake is treating it as a formality. The brief exists; the developer should just build. In practice, a 90-minute session going through real examples of the problem — actual customer questions, actual documents, actual edge cases — is worth more than a detailed written specification.
In iteration, the mistake is not defining what "done" looks like before building starts. Without agreed evaluation criteria, you end up with a subjective argument about whether the output is good enough.
In hardening, the mistake is treating it as optional. The system that works in staging breaks in production when it encounters data formats, user behaviours, or load levels that were never tested.
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- AI developer vs ML engineer: who do you need?
- Our tech consulting services
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.
Frequently Asked Questions
How much does it cost to hire an AI developer?
Freelance AI developers typically charge $80–$200/hour depending on experience and location. A project-based engagement for a custom AI system — from scoping through launch — usually runs $20,000–$100,000 for a mid-complexity build. Simple LLM integrations cost less; multi-agent systems with custom retrieval, integrations, and monitoring cost more. Always ask for a scoped estimate after the developer has reviewed your actual requirements, not a ballpark from a 30-minute call.
What is the difference between an AI developer and a machine learning engineer?
A machine learning engineer typically trains and fine-tunes models — building the underlying model itself. An AI developer (or AI engineer) integrates existing models — GPT-4, Claude, Mistral — into working applications. Most businesses do not need custom model training. They need an AI developer who knows how to use foundation models effectively within a production system. If you are not processing billions of data points in a highly specialised domain, you almost certainly need an AI developer, not an ML engineer.
How long does it take to build a custom AI system?
A realistic timeline for a working production system is 8–16 weeks from kick-off to launch, depending on complexity. This assumes reasonable access to your team during discovery, clean enough data to work with, and a defined scope. Projects slip when scope changes mid-build, data quality turns out to be worse than expected, or integrations with internal systems are more complicated than anticipated. Ask any developer you evaluate to show you project timelines from previous work, including where and why they shifted.
How do I know if an AI developer is actually experienced or just hype?
Ask them to describe a project that went wrong and what they did about it. Experienced developers have specific failure stories — a retrieval system that degraded at scale, a prompt that worked in testing but broke in production, a client whose data turned out to be too inconsistent to build on. Developers without real experience will either have no failure stories or will describe vague challenges with no technical specifics. Also ask to speak to a client whose system is currently running in production.
Do I need to provide training data to build an AI system?
For most LLM-based systems, no — you do not train a new model from scratch. But you do need to provide the data that the AI will work with: your product documentation, your customer FAQs, your internal knowledge base, your historical records. The quality of this data directly determines the quality of the system. A common failure mode is discovering mid-project that the data is inconsistent, incomplete, or formatted in ways that break the retrieval pipeline. Before starting any AI project, have an honest assessment of the state of your data.
What should I expect in terms of post-launch support?
Any serious AI developer will include at minimum a 30-day post-launch support period for bug fixes. Beyond that, AI systems in production require ongoing attention: model providers update their APIs, data drifts, usage patterns reveal edge cases that were not anticipated, and performance metrics need to be monitored. Ask specifically what monitoring is in place, who gets alerted when something breaks, and what the response process looks like. A system handed over with no monitoring infrastructure is a system you will be debugging blind.
Can an AI developer guarantee accuracy or performance levels?
No, and you should be suspicious of anyone who claims they can. Accuracy in AI systems is not a fixed property — it depends on the specific query, the quality of the data, the complexity of the request, and factors outside any developer's control. What a good developer can promise is a defined evaluation methodology, a baseline measurement at launch, and a process for identifying and improving underperforming areas. "95% accurate" is a marketing claim. "Here is our eval dataset, here is how we measure, and here is how we track drift over time" is an engineering answer.