AI · Prompt Engineering
Prompts are code. Treat them that way.
System prompts, structured output, function-calling, and evaluation suites — versioned, tested, and observable in production.
Overview
Behavior changes should be reviewed, not discovered. We bring software-engineering discipline to the parts of an AI system written in English.
What it is
The interface between intent and model.
Prompt engineering is the design of the inputs that steer a model: the system prompt that sets behavior, the output schema that constrains it, the few-shot examples that teach it edge cases, and the function definitions that let it act in the world.
Most teams iterate on prompts in chat windows and ship the result. We build the lifecycle around them: source control, evaluation, review, deployment, and monitoring — the same way any other production code earns its place.
Workflow
The prompt lifecycle, end to end.
- Author drafts a prompt.
- Version commits it under source control with a tag.
- Eval runs it against a golden set; results gate the change.
- Review approves the change with diff and eval delta visible.
- Deploy ships it behind a flag, staged across environments.
- Monitor watches for drift, cost, and quality regression — feeding findings back to authoring.
Deliverables
What you walk away with.
- Prompt repository with versioning, owners, and changelog — prompts as first-class artifacts.
- Evaluation suite: golden examples, regression tests, and per-version score history.
- Structured-output schemas with validators and graceful-degradation behavior.
- Function-calling / tool-use designs that fail safely when the model hallucinates a call.
- Production monitoring: prompt-version tags on every request, drift alerts, and rollback path.
Pitfalls
How we don't do it.
- Editing prompts directly in production without a review or rollback story.
- Treating "it worked once" as evidence — no eval, no regression catch.
- Cramming everything into a single mega-prompt instead of decomposing the task.
- Trusting free-form output where a typed schema and validator would do.
Engagement
How we work with you.
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01
Discover
Tasks, success criteria, and the failure modes you cannot tolerate.
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02
Architect
Prompt structure, output schema, and the evaluation set that defines done.
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03
Build
Versioned prompts, validators, eval suite, and review workflow in CI.
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04
Operate
Production monitoring, drift detection, and a steady cadence of regression review.
Want prompts that survive review?
Bring us a model behavior you cannot afford to regress. We'll build the eval set, the workflow, and the monitoring around it.
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