Free Lesson
The Frontier of Retrieval Engineering in Enterprise Systems
Part of FDE Builder Mode
60 min
Oct 27, 2026 12:00 PM
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What you'll learn
Why "retrieval" is the bottleneck in most AI systems.
AI systems rely on search algorithms to find relevant context. Out-of-the box algorithms usually don't get the job done.
Moving beyond Lexical, Semantic, and Hybrid Search
Most AI systems use basic algorithms that don't property integrate needed context. We'll discuss modern improvements.
Late Interaction and Multivector Search
Learn how multivector representations improve retrieval, and techniques to scale them for efficient production use.
Behavioral Vector Spaces & Reflected Intelligence
See how user interaction date enables automated feedback loops, personalization, and better contextual understanding.
Why this topic matters
For Forward Deployed Engineers, an AI agent is only as useful as the context it can retrieve. When deploying AI into real customer workflows, basic keyword search (BM25), semantic search, or even hybrid search may fall short of what the task demands. Strong retrieval engineering means understanding the customer’s data, tuning search to their domain, and measuring whether the results help users and agents get the job done.
