AI Engineer World's Fair 202619:49
Pinecone 2.0 — Edo Liberty, Pinecone
Edo Liberty
ML fundamentals · Context engineering · Retrieval-augmented generation
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AI Engineer topic
Natural-language database queries
10 AI Engineer conference talks about Natural-language database queries.
AI Engineer NYC 2026 Online Talks10:28
Every Building Is a Balance Sheet: Spatial Memory for Real-World Assets — Jordi Spranger, Spranic
Jordi Spranger
Agent memory · Data quality & curation · Knowledge graphs
20:26
Build-Time vs. Run-Time: Why Dev Tools Fail in Production — Averi Kitsch & Prerna Kakkar, Google
Averi Kitsch · Prerna Kakkar
AI security · Agent auth · Natural-language database queries
17:34
How We Solved Agent Building — Andrew Qu, Vercel
Andrew Qu
Agent engineering · Agent skills · Natural-language database queries
AI Engineer World's Fair 202638:18
Beyond RAG: Build a Relational Context Engine from Scratch — Peter Werry, Unblocked
Peter Werry
Retrieval-augmented generation · Natural-language database queries · Privacy & data governance
AI Engineer World's Fair 202617:15
Small Models, Big Results: Training a Finance Agent for Under $500 — Charles Dickens, Snorkel AI
Charles Dickens
AI in finance · Fine-tuning · Synthetic data
AI Engineer World's Fair 202618:20
From Context to Memory: Your Agents Need a Real Memory Layer — Anders Swanson, Oracle
Anders Swanson
Agent engineering · Agent memory · Context engineering
AI Engineer World's Fair 202652:58
RAG Needs a Map: Using GraphRAG to Retrieve Connected Context — Nyah Macklin, Neo4j
Nyah Macklin
Retrieval-augmented generation · Vector search · Agent engineering
AI Engineer World's Fair 20261:46:21
The Data Context Layer for Agents — Yoni Michael & Brandon Callender, Typedef
Yoni Michael · Brandon Callender
Knowledge graphs · Coding agents · Code review & verification
AI Engineer World's Fair 202619:44
Beyond RAG: A Relational Context Engine That Cuts Token Burn — Unblocked
Peter Werry
Retrieval-augmented generation · Coding agents · Agent engineering