The AI Product Stack: LLM APIs, Harnesses, and Reliability
R1 · UNDERSTAND12 minThis course demystifies the AI product market by introducing a clear AI stack, helping you understand where different products fit and why they behave as they do. You will learn the critical distinction between raw LLM APIs and stateful harnesses, and how harnesses are essential for building reliable, auditable AI systems from probabilistic models.
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Syllabus
1. The stack, top to bottom (the MECE model)
- Introducing the AI Stack Layers
- The Critical Distinction: API vs. Harness
- Module 1 Quiz
2. LLM API vs. harness: the load-bearing distinction
- The Raw LLM API (Layer 3)
- The Harness: Making the Model a Worker (Layer 5)
- Harness in Action: The Agent Loop
- Module 2 Quiz
3. Why harnesses are needed: the determinism problem
- The Determinism Problem
- ForgeShift Advisory's Deterministic Orchestration
- Capability vs. Reliability
4. A taxonomy of surfaces (layer 6), and where real products sit
- Introducing the Surface Taxonomy
- Thin Wrappers and Assistant Surfaces
- General Agents and Coding Harnesses
- Vertical Agents and Deterministic Apps
- Classifying AI Products
- Module 4 Quiz
5. Workshop: sort the product
- Product Classification Scenarios
- Module Summary and Final Quiz