AI SDLC Consulting
Prompt engineering consulting for engineering teams
Most teams prompt the way they once wrote scripts: individually, from memory, with no tests. We help you turn the prompts and context that work into shared, versioned assets your whole team can reuse, review and improve - engineered and testable, like the rest of your code.
Who this is for
For teams where good prompts live in one person's notes
When a developer finds a prompt that works, it rarely reaches anyone else. When a model changes, nobody knows which prompts broke. Prompt engineering makes this knowledge shared and dependable.
Teams scaling AI use beyond early adopters
You want the whole team to benefit from what a few people have learned.
Teams building AI into internal tools or pipelines
Prompts run in CI, review bots or developer tools and need to behave predictably.
Leaders who want consistent quality
You need output that does not depend on who wrote the prompt or how they phrased it that day.
What we do
From ad-hoc prompts to engineered ones
We start with the prompts your team already uses and make them better, not with a generic catalogue.
Collect and review
Gather the prompts and context people actually use, and identify which tasks they serve and where results fall short.
Define reusable patterns
Turn what works into templates for common tasks - implementing a change, writing tests, reviewing a diff, explaining code.
Engineer the context
Decide what repository conventions, architecture notes and examples each prompt should include, and keep them current.
Build a shared library
Store prompts in your repositories with owners, descriptions and usage notes so they are easy to find and reuse.
Version and review
Treat prompt changes like code changes, with pull requests, history and a clear record of why each change was made.
Evaluate automatically
Add test cases and scoring so you can tell whether a prompt, context or model change made results better or worse.
What you get
What your team keeps
Assets that live with your code and grow with your practice.
- Prompt library. reviewed templates for your most common engineering tasks.
- Context guidelines. what to include, what to leave out and how to keep it current.
- Versioning convention. how prompts are named, changed, reviewed and released.
- Evaluation suite. test cases and scoring for the prompts that matter most.
- Contribution guide. how anyone on the team adds or improves a prompt.
How it works
How a prompt engineering engagement runs
Step 01
Audit
We collect current prompts, interview users and pick the tasks where better prompts will matter most.
Step 02
Engineer
We rewrite and test prompts for those tasks, set up the library and agree the versioning convention.
Step 03
Embed
We coach the team to contribute, review and evaluate prompts themselves, then hand over.
FAQ
Frequently asked questions
Yes, though its focus shifts. Better models need less coaxing but still need the right context, constraints and examples, and you still need to know when a change makes results worse.
Partly. We design prompts to be as portable as practical and use the evaluation suite to catch regressions when you change models, which pairs well with Model Selection.
Prompts and context feed the generate and review stages of loop engineering - the deliberate design of how people and AI tools work together on a change - plan, generate, review, test, refine - with clear hand-offs, guardrails and review gates.
Related offerings
Keep exploring.
AI SDLC Consulting
All seven offerings for AI-enabled software delivery.
Loop Engineering
A plan, generate, review, test, refine loop your team can trust.
Spec-Driven Development
Specs that people and models can both execute against.
Model Selection
The right model for each engineering task, chosen on evidence.
AI Training for Teams
Hands-on programmes that make good practice a habit.
Contact Stavir
Tell us how your team works with AI today.