Prompt Engineering
Somewhere in your company is a 4,000-word prompt that nobody fully understands, that broke twice after "small tweaks," and that one engineer is afraid to touch. That's not prompt engineering — that's folklore. It works until the day it doesn't, and no one can say why either way.
We treat prompts as production artifacts: versioned in git, tested against eval suites, changed through review, and structured so the next engineer can read them. Add real context engineering — what the model sees, in what order, from which retrieval sources — and prompt quality stops being a personality trait and becomes a process.
Prompt architecture
Structured, modular prompts with clear contracts — readable, testable, maintainable.
Eval-gated prompt changes
Every edit scored against golden datasets before it ships. No more vibes-based tweaking.
Context engineering
Retrieval, ordering, and token budgeting so the model sees exactly what it needs.
Model migration hardening
Prompts rebuilt to survive model upgrades instead of breaking on release day.
Prompt ops tooling
Versioning, A/B testing, and rollback for prompts, wired into your CI.
Is prompt engineering still a thing, or do better models make it obsolete?
The party trick prompts died; the engineering didn't. Modern prompt work is context engineering — structuring instructions, retrieval, and tools so the model has what it needs. Better models raise the ceiling; disciplined prompts are how you reach it.
Our prompts break every time we upgrade models. Normal?
Common, and self-inflicted. Prompts that lean on one model's quirks shatter on upgrade. We write to documented behavior, keep instructions explicit, and run migrations through evals — so a model upgrade is an afternoon, not an incident.
When is fine-tuning better than prompting?
After prompting with good retrieval has plateaued, and only with eval data to prove the gap. Nine times out of ten, teams asking for fine-tuning need better context engineering first. It's cheaper, faster, and reversible.
Put Prompt Engineering to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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