Service
Prompt Engineering
Same AI, same data, but 90% accuracy instead of 40%. The difference is how you ask.
Overview
A well-engineered prompt is the fastest lever to improve AI output quality. We treat prompts as code: versioned in Git, tested against hundreds of real examples from your domain, and optimized per model. Claude, GPT, and open-source models respond to different patterns, so we tailor strategies to each. Changes are measured against baselines before they ship, and cost optimization ensures you're not overspending on tokens for tasks a smaller model handles just as well.
Capabilities
Model-Specific Design
Each model family requires its own approach. We select and combine techniques like chain-of-thought reasoning, few-shot examples, and structured output formatting based on what actually works for your task and your target model.
Automated Evaluation
Each prompt change runs against a suite of hundreds of test cases drawn from your domain. Accuracy, coherence, safety, and task-specific metrics are measured automatically. Regressions are caught before they reach users.
A/B Testing & Versioning
Multiple prompt variants run side by side in production with traffic splitting. Statistical analysis determines the winner with confidence intervals. No change ships based on gut feeling.
Cost Optimization
We implement prompt caching, token-efficient formatting, and intelligent model routing. Simple requests go to fast, affordable models while complex tasks go to frontier models, cutting costs without cutting quality.
Deliverables
- Optimized prompt library with documentation and version history
- Evaluation framework with automated CI/CD integration
- Performance report with baseline comparisons and cost analysis
Tech Stack
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