The Retrieval Layer for Edge AI
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Sasha Denisov
Brainform.ai
CTO and Co-founder
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Chadha Sridi
Qdrant
Developer Relations Engineer
Context engineering is the art of orchestrating the information your AI systems have at their disposal. AI agents’ knowledge is only as good as the domain knowledge they can access, and how you build that knowledge retrieval layer determines everything downstream.
This workshop approaches context engineering from a vector search standpoint, covering the range of tooling it enables (f.e., hybrid search), where it genuinely fits and where it doesn’t, and what complements it (knowledge graphs behind GraphRAG).
We’ll go from the intuition behind context engineering to context engineering in practice. You’ll see the full picture: how to increase search relevance and reduce retrieval latency, and how to orchestrate context-engineering tools.
By the end of the workshop, you’ll build a context-engineering layer of a simple medical AI copilot.
World Congress 2026 Europe
Sasha Denisov
Brainform.ai
CTO and Co-founder
Chadha Sridi
Qdrant
Developer Relations Engineer
Raphael De Lio
Redis
Software Engineer
Samuel Agbede
Redis
Developer Advocate
David vonThenen
NetApp
Senior AI/ML Engineer
Gal Shubeli
FalkorDB
AI Engineer