AI Engineering

RAG, agents, tool use and MCP — architecture for LLM-powered systems.

Ground LLM answers in your own data — ingestion, chunking, embeddings, vector search, reranking, prompting with citations, and evaluation.

LLMs that plan and act through tools in a loop — workflows vs agents, the agent loop, guardrails, and when not to build an agent.

An open protocol that standardises how AI apps connect to tools and data — build a server once, use it from any MCP-capable client.

Test AI features like software — golden datasets, code-based and LLM-as-judge graders, regression gates in CI, and production monitoring.