Context construction, prompt contracts, tool calls, schema validation, retries, and observable model APIs.
For a small example, convert a sentence into a typed event with date and location. Define a schema, request structured output, reject invalid values, and retry only with bounded, observable rules.
A model chooses between search, calculator, and ticket creation. Describe tools precisely, validate arguments, separate read from write permissions, log outcomes, and require approval for side effects.
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Predict how one control changes the LLM Interfaces & Structured Outputs result.
The starter deliberately follows a tempting but flawed assumption: Parsing free-form output with ad hoc regular expressions
Check: The browser checks pass and you can name the failure mechanism before opening the explanation.
Failing starter lab
Accept only objects that contain required keys with non-empty string values.
Run the incomplete starter before opening hints. Use the first wrong output or failed check to form a repair hypothesis.
Explanations after each answer · 80% to pass · LLM Interfaces & Structured Outputs
Chapter 1 of 4 · Core
An LLM API is a probabilistic dependency inside a deterministic software system. A robust request contract defines the task, permitted context, output schema, tool surface, latency budget, token budget, retry policy, and failure behavior. Prompt text alone is not that contract.
The input context is assembled from instructions, conversation state, retrieved evidence, tool definitions, and the current user request. Each part competes for a finite token window. More context can increase cost and distract the model, so context selection should be evaluated as carefully as model selection.
Version prompts and schemas like code. Record the model identifier, decoding parameters, prompt version, tool versions, and input hashes for every trace. That lineage makes regressions reproducible and allows an evaluator to distinguish model drift from application changes.
Two free references selected for LLM Interfaces & Structured Outputs.
Primary sources and free technical reading for LLM Interfaces & Structured Outputs.
A vendor-neutral introduction to typed JSON contracts and validation constraints.
Practical Python validation, strict models, custom validators, and serialization.
Research on language models learning when and how to invoke external tools.
A practical guide to subword tokenization families and their tradeoffs.