GenAI Project Development & LLM Application Engineering
From prototype to production-grade generative AI systems.
Generative AI moves fast, and most GenAI projects stall between an impressive demo and a reliable product. I design and ship production-grade LLM applications end to end — model selection, prompt architecture, evaluation harnesses, guardrails, cost optimization, and observability — so your generative AI actually holds up under real users and real traffic.
What building a GenAI project actually involves
A production LLM application is far more than a chat wrapper. It is a system of prompts, retrieval, tools, memory, evaluation, and monitoring working together. I build each layer deliberately so the whole thing is measurable and maintainable.
- Model selection and benchmarking across Claude, GPT, Gemini, and open-weight models
- Prompt engineering with structured outputs, tool calling, and JSON schemas
- Evaluation harnesses and LLM-as-judge pipelines to prevent silent regressions
- Guardrails, PII handling, and safety filtering for enterprise compliance
- Token-cost optimization, caching, and latency tuning
How I deliver LLM applications that survive production
Every engagement starts with a measurable objective, not a model. I instrument quality from day one with golden datasets and automated evals, then iterate on prompts, retrieval, and orchestration until the numbers move. The result is a GenAI system you can trust, extend, and hand to your team.
Technologies I use
Related case studies
Frequently asked questions
What is a GenAI project?
A GenAI (generative AI) project builds an application powered by large language models or other generative models — for example a chatbot, RAG assistant, autonomous agent, or content pipeline. Production GenAI projects include prompt engineering, retrieval, evaluation, guardrails, and monitoring, not just a single model call.
Which LLM should I use for my project?
It depends on your accuracy, latency, cost, and privacy needs. I benchmark candidates — Claude, GPT, Gemini, and open-weight models like Llama and Gemma — against your own evaluation set so the choice is driven by data, not hype.
How do you keep LLM quality from regressing?
I build automated evaluation harnesses with golden datasets and LLM-as-judge scoring so every prompt or model change is measured before it ships. This catches silent regressions that manual testing misses.
Ready to build with genai development?
Let's scope your project and turn it into a production system.
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