Intro I design and deliver production-grade Retrieval-Augmented Generation (RAG) platforms that connect large language models to an organization’s own knowledge, turning static documentation into a live, self-service knowledge assistant.
What I Deliver
- RAG architecture design: chunking, embeddings, and vector store selection
- LLM integration with LangChain and LangGraph orchestration
- Evaluation pipelines and guardrails for accuracy and safety
- Production deployment across AWS, Azure, and GCP
- Cost and latency optimization for LLM workloads
How I Work I start from the business question the knowledge assistant needs to answer, then work backwards through retrieval quality, prompt design, and evaluation loops before scaling to production.
Why It Matters A well-built RAG platform turns scattered institutional knowledge into instant, trustworthy answers, reducing time lost searching for information and improving decision speed.