LLM API across the ecosystem.

Start with the broad neural API introduction, then read structured outputs before moving into retrieval or observability. These articles follow the same application boundary from different angles: what should a request mean, which evidence should support its result, and how should the system behave when the result is unusable?

Use this collection to compare how the same design question changes across tasks. Read the source-backed explanation before adapting an example. Identify which assumptions apply to your environment, which information is unavailable, and which checks must remain in ordinary application code. A reference pattern is a starting point for that review, not evidence that a connected service or deployment already exists.

Keep a small set of difficult cases as you explore: an incomplete input, conflicting evidence, a changed permission, and an operation whose result is uncertain. Record what should happen in each case. When you finish a guide, revisit those expectations instead of judging the design only by its most fluent answer. The linked articles offer complementary perspectives, so a useful reading order follows the consequences of your task rather than the publication date alone.