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The LLM Triangle: Engineering Principles for Robust AI Applications - Almog Baku
Original: The LLM Triangle: Engineering Principles for Robust AI Applications - Almog Baku:
Takeaway
Robust LLM apps come from translating expert SOPs into agent+code pipelines, treating the model as a procedurally-instructed intern rather than a magic black box.
Summary
- LLM Triangle Principles: model + engineering techniques + data, all guided by a Standard Operating Procedure (SOP) borrowed from manufacturing
- Treat the LLM as a smart but inexperienced intern; give it step-by-step recipes derived from interviewing or simulating a domain expert
- LLM-native architecture = decompose SOP into a graph of agents and deterministic code steps (e.g. Wikipedia writer: distill→categorize→search→markdown→TOC→sections)
- Distinguishes imperative/reusable/recursive agents and autonomous agents; argues LLM-native apps are 10% model and 90% data-driven engineering
agentsarchitecturesop
Original description
Let's face it: most LLM App PoCs are a disaster waiting to happen. Hallucinations, inconsistency, and scalability nightmares abound. Enter the LLM Triangle - a framework for building reliable, production-ready AI systems. I'll present the LLM Triangle Principles, a framework for building robust LLM-native applications. Based on extensive experience, I'll share key insights for bridging the gap between LLM potential and production-grade performance. We'll explore: - SOPs for consistent LLM performance - Strategic model selection balancing capability and cost - LLM-native architecture for production - Contextual data optimization techniques Using real-world implementations, we'll tackle common pitfalls and present innovative solutions. Senior engineers and tech leaders will gain actionable insights to elevate LLM applications from concept to production. Join us to gain practical strategies for robust and scalable AI systems.