Retrieval-augmented generation looks simple in a demo and falls apart in production — recall drops, latency spikes, and relevance tuning becomes guesswork. This talk walks through building a real RAG pipeline for agents on Microsoft Foundry, backed by Azure AI Search: HNSW vector index configuration, hybrid search profiles, and the retrieval tuning decisions that actually move the needle on answer quality once a Foundry agent is grounding its responses in your data at scale.
What You'll Learn:

- Vector index fundamentals: Configuring HNSW parameters and hybrid search profiles in Azure AI Search for use as a Foundry agent's retrieval layer
- Where RAG breaks in production: Recall degradation, latency spikes, and relevance drift that don't show up in a demo but surface immediately at scale
- Retrieval tuning that actually moves the needle: Chunking strategy, embedding choice, and reranking decisions, evaluated against real answer-quality metrics
- Grounding Foundry agents reliably: Connecting retrieval quality to agent evaluation — how bad retrieval quietly becomes bad agent behavior downstream
- Debugging retrieval failures: A practical framework for diagnosing whether a bad agent answer is a retrieval problem, a prompt problem, or a model problem