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Understanding AI Stakes to Break Production Code: Philip Rathle
Takeaway
Match your retrieval architecture (vector → graph) to the stakes of the use case, and let LLMs orchestrate deterministic tools when the answer must be exact.
Summary
- Rathle (Neo4j) frames an 'application stakes' spectrum: low-stakes summarization (kids bedtime story) → mid-stakes copilot with human-in-loop → high-stakes pilot mode (Boeing 737 Max bolt-torque-grade tolerance for error).
- Pattern of unlocks for higher-stakes: vanilla LLM → vector RAG → graph RAG/knowledge graph; each S-curve raises the bar on enterprise knowledge, freshness, hallucination control.
- Analogy: vectors give 'right-brain' statistical proximity reasoning (mostly right, sometimes very wrong, black box); knowledge graphs add 'left-brain' structured discernment, long-term memory, reasoning over facts.
- Roundtable lessons: scope by stakes early, kill more projects than you experiment, use the LLM to write code that solves the problem rather than solving it directly, retrain customers who default to keyword search.
graphragproduction-aistakes
Original description
When your mindblowing prototype meets the real world, GenAI projects can get stuck on their way to production. Whether the sticking points have to do with accuracy, explainability, compliance, cost, privacy, or something else, depends a lot on what’s at stake. In this round table session, we will explore “getting unstuck” and how that's different depending on project stakes... where "stakes" refers to what you have to gain or lose from good & bad answers. Let’s explore the bar for “getting to production” together through this lens--considering things like dollar impact, brand & reputational impact, privacy, bias, health & human safety, regulatory, etc. Bring your examples and questions and learn from each other’s successes and challenges to break the AI production code. Recorded live in San Francisco at the AI Engineer World's Fair. See the full schedule of talks at https://www.ai.engineer/worldsfair/2024/schedule & join us at the AI Engineer World's Fair in 2025! Get your tickets today at https://ai.engineer/2025 About Philip Philip Rathle is CTO of Neo4j, the graph database and analytics leader that enabled the ICIJ to crack the Panama Papers, and NASA to get to Mars two years faster. Neo4j enables thousands of organizations worldwide-- including most of the Fortune 500-- to solve their most pressing & valuable problems through the power of the connections in data. Philip comes from a long career in data & databases. He joined Neo4j in 2012, where he led Product Management for over a decade, helping to pioneer the graph database category and creating one of the world’s leading database & analytics companies.