The API glossary
Shared words.
Clearer meanings.
A working vocabulary for the field guides. Each definition links to the page where the concept becomes a design decision.
- Agentic workflow
- An application sequence in which a model may propose tool operations or further steps. In the designs here, controlled application code authorizes and executes those proposals. Explore agentic APIs →
- API contract
- The agreement about inputs, outputs, states, and errors at an interface. It should explain missing information and failure behavior as clearly as the successful path. Read the foundations →
- Authorization
- The application decision that a particular user or process may perform a specific operation on a resource. A valid model-generated request is not automatically authorized. Review permissions →
- Biosignal metadata
- Information that gives measurements context, including channel labels, units, sampling information, session identity, and timing semantics. Explore brain-data interfaces →
- Context window
- The bounded amount of input and output material that a particular model configuration can handle. Actual limits and counting rules must be verified with the provider. Design the context →
- Embedding
- A numerical representation of an input, used here as a building block for similarity-based retrieval. Similarity is not the same as evidence that an answer is correct. Read about embeddings →
- Evaluation
- A deliberate comparison between a system’s behavior and stated acceptance criteria. Separate syntax, evidence, permission, and task-completion failures. Measure accepted tasks →
- Inference
- Using a model to produce an output for an input. The surrounding API and application still need their own validation, permissions, and recovery behavior. Explore LLM APIs →
- JSON Schema
- A vocabulary for describing and validating the structure of JSON data. Schema validity does not establish that a value is supported by the source evidence. Design structured outputs →
- LLM
- A large language model. This site focuses on how applications package tasks and validate responses rather than claiming that fluent output guarantees understanding or accuracy. Follow the LLM track →
- Point-in-time data
- Information represented according to what was available at a specified time, rather than quietly incorporating later revisions or outcomes. Explore research timing →
- Prompt template
- A reviewed instruction structure with deliberately inserted variables. Version it with the model settings, schema, and evaluation fixtures that affect behavior. Explore prompt templates →
- Provenance
- The source and transformation history of a value, passage, or measurement. It helps a reviewer trace a derived result back to the material behind it. Preserve data meaning →
- RAG
- Retrieval-augmented generation: an application first retrieves relevant source material, then supplies it to a generation step. Both retrieval quality and answer support need evaluation. Inspect the evidence pipeline →
- Structured output
- A response in a defined machine-readable shape. Use application validation for the schema and separate checks for meaning, evidence, and business rules. Read the structured-output guide →
- Tool call
- A structured request to use an operation. For client-executed tools, the application receives the proposal, applies its checks, executes if permitted, and returns a result. Understand tool calling →
- Validation
- Checking a response or proposal against explicit requirements. Distinguish transport, syntax, structure, evidence, permission, and final completion. Plan the validation path →
These are working definitions for this learning collection. Use the linked guides and their primary references for implementation-specific meanings and constraints.