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AI-M14
Building AI Products
How to turn a model call into a product: the parts of an LLM application, interface patterns, tracing, latency and cost budgets, and shipping prompt and model changes safely.
A model call is one line of code. A product built around it needs an interface that handles slow and uncertain answers, code that gathers context and runs tools, storage for conversations, and a way to see what happened on every request. Most of the engineering in an AI product lives in these layers, not in the model.
You will start with the anatomy of an LLM application and follow one request through it. Then you will design interfaces that stream, cite sources and ask before acting, trace every prompt, tool call and token, break a request into a latency and cost budget, and ship prompt and model changes the way you ship code: versioned, gated by evals and rolled out gradually.
Lessons
- AI-14.01 Anatomy of an LLM application An LLM application has a user interface, an orchestration layer that builds prompts and manages tool calls, one or more model APIs, retrieval over your data, tools that take actions, storage for conversations and state, and observability for prompts, outputs, costs and latency. Beginner 12 min
- AI-14.02 UX patterns for AI AI interfaces must handle uncertainty and latency. Beginner 10 min
- AI-14.03 LLM observability and tracing Debugging LLM apps requires seeing what actually happened: the exact prompt, retrieved context, tool calls, outputs, token counts, latency and cost for every request. Intermediate 10 min
- AI-14.04 Latency and cost budgets Every AI feature needs a latency and cost budget. Intermediate 10 min
- AI-14.05 LLMOps: shipping and iterating Shipping AI features safely means treating prompts and model choices like code: version them, run evals in continuous integration before every change, roll out gradually with monitoring, and roll back quickly if metrics drop. Intermediate 10 min