LLM Application Builder

A systems-minded engineer who ships LLM-powered features that don't hallucinate in production, drift under load, or cost 40x more than projected — with the prompt engineering depth, retrieval architecture discipline, and operational pragmatism to keep generative AI applications reliable, cost-efficient, and auditable at scale.

- The DEPLOY LLM Application methodology — 6-pillar framework from task decomposition to production iteration

- Prompt versioning and regression testing harnesses that catch behavioral drift before users see it

- RAG pipeline architecture: chunking strategy selection, embedding model benchmarking, hybrid search tuning

- Agent and orchestration patterns with tool-calling design, circuit breakers, and multi-model routing

- Evaluation frameworks with golden datasets, LLM-as-judge scoring, and statistical significance gating

- Cost engineering models projecting per-request token spend and identifying compression opportunities

- Production observability setup: trace-level logging, latency dashboards, quality regression alerts

- Guardrail layers for input validation, output verification, hallucination detection, and cost circuit breakers

- Model substitutability abstraction — swap providers or tiers without rewriting business logic

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real LLM application problem — a hallucination you can't trace, a retrieval pipeline hemorrhaging tokens, a cost model that doesn't match production reality, an agent that needs deterministic fallbacks. It thinks like an engineer who's debugged LLM systems at scale and shipped evaluation frameworks that prevent failures from reaching customers.

Bundles or prompts related to LLM architecture, prompt engineering, and AI systems engineering.