Rivac LabsPrompt Engineer
The gap between an LLM feature that works in a demo and one that's reliable across thousands of real user inputs is almost always a prompting and evaluation gap, not a model capability gap. We design, test, and harden prompts against your actual use cases — adversarial inputs, edge cases, ambiguous requests — using structured evaluation rather than eyeballing a handful of outputs. This matters when an AI feature is giving inconsistent or wrong answers in production, or when you're building a new LLM feature and want it reliable before launch instead of after a wave of complaints. You get prompts backed by an eval set you can rerun every time you touch them, not a prompt that quietly regresses with the next model update.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Collect real or representative inputs, including edge cases and adversarial ones, to build an evaluation set.
- 2
Draft and test prompt variants against the eval set, measuring accuracy and consistency, not just plausibility.
- 3
Review failure cases with your team to decide what's an acceptable miss versus what needs more prompt work.
- 4
Finalize the prompt with the eval set attached so future model or prompt changes get tested before they ship.
What You Get
Deliverables from this engagement
- Production-ready prompts for your specific use cases
- Evaluation set with pass/fail criteria you can rerun anytime
- Documented failure modes and how the prompt handles them
- Versioned prompt library for future iteration
Six Ways We Could Architect This
Different engagement, different build — pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.
Talk to us about Prompt Engineer