Draft alt text from the image’s purpose, not everything AI can name
Choose informative, functional or decorative treatment, write the needed meaning and verify it in the page’s actual context.
Understand AI tools, check their output and build practical workflows with original sources and clear examples.
Search this section ↗96 topics in this section.
Choose informative, functional or decorative treatment, write the needed meaning and verify it in the page’s actual context.
List consequential claims, attach original sources and decide which statements are verified, qualified or removed before publication.
Specify the task, evidence and output, then test the result against concrete requirements rather than asking for “the best answer.”
Use the API’s supported instruction roles, keep quoted documents as data and test conflicting input before shipping an assistant.
Define a supported schema, handle refusals and incomplete output, then check business rules beyond successful JSON parsing.
Design narrow tools, validate arguments and permissions, execute the allowed action and return its real result.
Separate reading from writing, restrict accounts and destinations and require a clear authorization path for consequential actions.
Retrieve relevant permitted passages, attach source references and allow the answer to say when the documents do not support a claim.
Compare semantic retrieval with keyword search, preserve document permissions and evaluate whether the relevant evidence is returned.
Keep headings, clauses and table context together, then test whether a retrieved chunk can answer the question on its own.
Preserve source IDs and page references, attach citations to individual claims and verify that the cited passage actually supports them.
Keep reusable material stable, follow the selected model’s caching controls and check usage instead of assuming every repeat is discounted.
Prepare independent tasks with stable identifiers, track job status and recover partial failures without resubmitting successful items.
Handle documented stream events, show an unfinished state and wait for completion before parsing or acting on the result.
Measure queueing, retrieval, first output and completion separately, then reduce the stage that is actually delaying the task.
Keep a representative test set, define what counts as success and compare a new prompt or model without changing the test at the same time.
Use harmless fixtures for tool timeouts, conflicting evidence and denied actions, then verify that the application stops without inventing success.
Keep the provider key server-side, authorize users and limit request size, model access and usage before exposing an AI feature.