Prompt Engineering
Your AI Feature Isn't Broken. Your Prompts Are.
I diagnose and rebuild underperforming prompts and AI outputs into reliable, tested, production-grade systems — with evals so you know it's actually better, not just different.
What's Included
- ✓ Audit of your current prompts/outputs against real failure cases you're already seeing
- ✓ Rebuilt prompt architecture: system prompts, few-shot examples, structured output formatting
- ✓ Eval suite built around your actual use case, not generic benchmarks
- ✓ Cost/latency optimization alongside quality — a better prompt that's also cheaper to run
- ✓ Documentation so your team can maintain and iterate on prompts after handover
Why It Matters
- → Fewer hallucinations and off-brand/off-spec outputs reaching your users
- → Measurable proof of improvement via evals, not just 'it feels better'
- → Lower per-request cost from tighter, more efficient prompts
- → A maintainable prompt system your team can keep improving, not a black box
Process
- 1
Failure audit — collect and categorize the outputs that are currently going wrong
- 2
Eval design — build a test set that actually reflects your real use case
- 3
Prompt rebuild — iterate against the eval set until quality/consistency targets are hit
- 4
Cost/latency pass — tighten the winning version for production economics
- 5
Handover — documented prompt system + eval suite your team owns going forward
Pricing
Single Use Case
from $500
One prompt/workflow rebuilt and eval-tested
Prompt System Overhaul
from $1,800
Full audit across multiple AI touchpoints in your product
Ongoing Prompt Maintenance
from $250/mo
Monthly tuning as your product/model versions evolve
What Clients Say
“[Result — e.g. 'error rate dropped 40% on the same model']”
— [Client Name, Role, Company]
“[Result]”
— [Client Name, Company]
FAQ
We're already using GPT/Claude — why would this help?
Most quality problems are prompt/architecture issues, not model limitations — this fixes the layer you actually control.
Do you switch us to a different AI model?
Only if the eval data shows it's genuinely better for your case — never a default recommendation.
What's an 'eval suite' in plain terms?
A repeatable test set that scores AI output quality, so improvements are measured, not guessed at.
Is this useful if we're pre-launch?
Yes — cheaper to fix prompt reliability before launch than after users notice.
How is this different from AI Agents?
This is about output quality for a defined task; AI Agents is about building autonomous multi-step systems — prompt engineering is often step one of an agent build.