Ground OpenAI answers with web search: citations, context size, and verification

Web search, with receipts

The steps

1. Enable the web_search tool in the Responses API tools array. The model decides when to search based on the input; for questions about current events, tell it explicitly that freshness matters.
2. Set search_context_size deliberately: low for simple lookups, medium as the default, high when the answer needs detail from multiple sources. Bigger context costs more; do not default to high everywhere.
3. Render citations. The model returns inline citations plus url_citation annotations with URL, title, and location. These must be visible and clickable in your UI; stripping them turns a grounded answer into an ungrounded one.
4. Verify high-stakes claims against the cited sources. The model can misread a source or cite one that does not support the claim. For medical, legal, or financial answers, a human or a second pass should open the top citations.
5. Tell the model which sources to prefer in the instructions: primary sources, official docs, recent dates. Without guidance it may cite a content farm that outranks the official page.
6. Cache carefully. Search results go stale; a cached answer with last month's citations is worse than a slower fresh one. Keep TTLs short for time-sensitive topics.

The trap

Treating a web-search answer as verified because it has citations. Citations are pointers, not proof. The workflow is: search, cite, then check the citations for anything that matters.

Checklist

• web_search enabled where freshness matters.
• searchcontextsize set per use case, not defaulted.
• Citations rendered visible and clickable.
• High-stakes claims verified against sources.
• Source preferences in the instructions; short TTLs on cached answers.

OpenAI's web search guide documents the websearch tool, searchcontextsize low/medium/high, and urlcitation annotations that must be visible and clickable. Verified live at https://developers.openai.com/api/docs/guides/tools-web-search.