AI Systems · core
Structured Outputs
Forcing LLM output into a validated JSON schema.
Mental model
Free text is unparseable glue between systems. Structured outputs constrain generation to a schema (via grammar/JSON mode) so the model's answer is a typed object your code can trust — or reject.
How to study Structured Outputs
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use OpenAI — Structured Outputs to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Structured Outputs with Tool Calling, LLM Evals. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Schema-constrained extraction and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Where it matters
Function arguments, extraction pipelines, agent state, every LLM-to-code boundary.
Common mistakes
- Parsing JSON out of prose with regex instead of schema mode
- No validation step, so malformed output crashes downstream
- Over-nesting schemas the model cannot reliably fill
Learn from primary sources
Practice and explain it back
Schema-constrained extraction
Define a JSON schema and use structured-output mode to extract typed fields from unstructured text. Validate the result and reject on failure.
Expected evidence: A validated typed object, or an explicit rejection.
Open the interactive drill →Review prompts
- Why constrain LLM output to a schema instead of parsing prose?
Build evidence
LLM eval harness
A dataset + grader harness that scores LLM output on every prompt/model change.
- Fixed dataset of inputs with expected behavior
- At least one deterministic grader and one LLM-as-judge grader
- Runs as a single command and prints a score
Prerequisites
None assigned yet.