Prompt Engineering
Enterprise prompt systems, RAG architectures, and AI tool design. Reliable, repeatable output your team can trust.
The difference between AI that works and AI that’s useful.
Every company has access to GPT-4, Claude, Gemini. The difference isn’t the model — it’s how you use it. Prompt engineering is designing inputs that consistently produce high-quality, reliable outputs.
I design prompt systems and RAG architectures that turn general-purpose AI into domain-specific tools tailored to your business.
What enterprise prompt engineering involves
Prompt system design
Individual prompts are brittle. I design prompt systems — chains that handle edge cases, validate outputs, and maintain consistency across thousands of uses. System prompts, few-shot examples, output formatting, and error handling.
RAG architecture
Retrieval-Augmented Generation grounds AI in your data — product docs, knowledge bases, support tickets. I design RAG systems with proper chunking, embedding models, vector database selection, and retrieval optimization.
AI tool & agent design
Custom AI tools for specific functions: content generation, data analysis, customer support, code review. Each tool has guardrails, monitoring, and human oversight built in.
Evaluation & testing
Automated testing, quality metrics, regression detection, and benchmarking against human performance. You can’t improve what you can’t measure.
Model selection & cost optimization
GPT-4 isn’t always the right choice. I evaluate models against your requirements and optimize for the best quality-to-cost ratio.
Who this is for
Teams past the “try ChatGPT” phase. Companies building AI into products. Teams that want to automate knowledge work without sacrificing quality.