When an LLM contract is the right buy
The usual starting point is a product team that has already proved an LLM feature can work. There is a demo, perhaps an internal beta, and a funded backlog: summarise this record, extract fields from that document, draft a reply, classify incoming requests. What there is not is someone who has taken this kind of feature to production before. The prototype returns malformed JSON one time in fifty, quality shifts when the provider updates a model, nobody can say whether last week’s prompt change helped, and the cost per request is a guess.
You do not need a separate project team for this. You need a senior engineer who has done it before, sitting in your team, implementing the backlog in your codebase and leaving the patterns behind.
What the work involves
Most LLM backlogs share a handful of engineering problems underneath the feature names. I work through them in the order your manager sets, but they tend to look like this:
- Output you can trust structurally. Every model response that feeds code is validated against a schema. Refusals, truncation and malformed output have explicit paths: retry, repair, fall back or fail visibly. Nothing writes bad data quietly.
- Quality you can measure. Each feature gets a small evaluation set built with the people who know what a good answer looks like. It runs in CI, so a prompt edit or model change shows its effect in the pull request.
- Change you can trace. Prompts and model identifiers live in version control. A quality regression can be traced to a specific change rather than argued about.
- Cost and latency you can see. Tokens, latency and failure rates per feature go into your existing monitoring, with budgets your team agrees.
- Data you can account for. What is sent to which provider, what is logged and for how long, written down and enforced in code.
The signature deliverable
The contract starts and ends with documents your team keeps: a named contractor remit, a delivery backlog, the access prerequisites and a handover plan. The remit matters most, because it stops the role drifting. Illustrative example of a remit extract:
| Area | I own | I advise | Team owns |
|---|---|---|---|
| Ticket-summary feature | Implementation, evaluation set, schema validation | Prompt wording with support leads | Release decision |
| Provider interface | Design and first implementation | Choice of fallback model | Contract with the provider |
| Logging and redaction | Implementation to your policy | Retention period | The policy itself |
| CI evaluation gate | Harness and thresholds proposal | Threshold values | Enforcing the gate |
Illustrative example showing the format, not a record from a client engagement.
The access prerequisites list sits alongside it: each system, the level of access, who approves it, and the date it is needed. Contracts most often lose their first weeks to access, so this list is agreed before day one where possible.
How acceptance is judged
Your engineering manager accepts each change through normal review. Every LLM feature change carries an evaluation run in the pull request, compared against the previous baseline, so acceptance is based on evidence rather than a reviewer reading a few outputs. At the end, the measurable output is the shipped features, the evaluation suite your team can run, and a backlog with what is left in priority order.
Ownership and handover
Everything is built in your repositories, under your conventions, reviewed by your engineers. I agree the handover plan with your manager around the midpoint, naming who will own each area, and then work towards it: those engineers pair on the relevant changes and run the evaluation suite themselves before I leave. The written handover covers how to add an evaluation case, how to change a prompt or model safely, the provider interface, the remaining backlog and known risks.
When to choose something else
If the hard part is retrieval, the RAG engineer contract is a better fit. If the features take actions in other systems, see the AI agent engineer role. If you want a quality gate delivered as a fixed outcome, commission production LLM evaluation. If you are moving between providers, LLM provider migration is scoped for exactly that. For a smaller recurring load, consider part-time AI engineering capacity.