AI Planning Frameworks

Quick Guide: Default to the lowest tier that meets the requirement — most features are one well-built call — and name the requirement behind every tier above it. Pin the model id, give every output a contract the code checks on receipt, state budgets in tokens and money, give each failure the feature can hit a row, and fix eval thresholds before the first run. Two provider facts shape the answers: a schema-constrained output mode guarantees shape only for a response that completed and was not refused, so the contract's remaining work is truncation, refusals and values; and reasoning tokens bill as output and count against the output ceiling, so a ceiling sized for the visible answer can run out before one appears.

Detailed Resources:

• examples/core.md — templates for the sections every model-calling feature needs, and a worked specification
• examples/retrieval-and-agents.md — retrieval design, tool contracts and loop guards
• reference.md — output-mode guarantees, budget line items, default failure behaviour, retrieval starting points, grading methods, eval set sizing

Which path applies

A section belongs in the spec only when the feature has its artifact; one filled in for completeness reads to the implementer as something to build.

• The feature calls a model — approach, model, prompt, output contract, budgets, failure modes and eval plan; follow examples/core.md.
• It retrieves from a corpus — add the retrieval section from examples/retrieval-and-agents.md.
• The model calls tools, or orders its own steps — add tool contracts and loop guards from examples/retrieval-and-agents.md.