Precision before scale.
Expert prompt design, model selection, and evaluation for production use cases. The difference between a demo that impresses and a system that runs.
- 01
Baseline audit
Current prompts and model usage reviewed against production targets. Failure modes, hallucination patterns, and latency bottlenecks surfaced before any changes are made.
- 02
Prompt architecture
System prompts, chain-of-thought, few-shot structures, and output formatting designed for your specific task. No copy-paste from the internet.
- 03
Model selection
GPT-4o, Claude, Gemini, Llama, Mistral: matched to task complexity, data sensitivity, budget, and latency. We run evaluations, not assumptions.
- 04
Evaluation framework
Structured test sets and scoring criteria for your use case. Regression testing so improvements don't break what's already working.
- 05
Workshops & enablement
Team sessions that build internal prompt literacy. Not theory, real tasks from your workflows, worked live.
Numbers that matter.
Real engagement data and client results, not projections.
Every engagement ships.
Prompt library
Documented, version-controlled system prompts, few-shot examples, and chain-of-thought patterns ready to deploy.
LLM evaluation report
Side-by-side comparison of models (GPT-4o, Claude, Gemini, Llama, Mistral) against your specific use case and quality bar.
System prompt architecture
Modular prompt structure that separates persona, instruction, context, and output format, maintainable by your team.
Quality baseline
Evaluation dataset and automated quality checks so you know when a model update degrades output.
Ready to start?
Questions,
straight.
Common questions about Prompt Engineering. If yours isn’t here, ask us directly.