THE SHORT ANSWER
A system may crawl pages into a search index, retrieve results when a question is asked, provide selected passages to a model, generate a grounded response and display references. Model training is a different process that can influence general capabilities without providing live access or a citation trail. Architectures vary, so verify the documented behavior of each platform.
Follow one possible retrieval path
| Stage | Purpose | What it does not guarantee |
|---|---|---|
| Crawl | Fetch accessible content | Indexing or permitted reuse |
| Index | Organize information for search | Selection for a query |
| Retrieve | Find candidates relevant to the current question | Correctness or completeness |
| Ground | Supply selected material to generation | Faithful interpretation |
| Generate | Compose a response | A source visit |
| Reference | Expose supporting links or citations | Endorsement or attribution of every claim |
Google documents retrieval from its Search index and query fan-out for current AI Search features. This is a platform-specific description, not a blueprint for every assistant.
Evidence & context: Google Search Central · Google Search Central
Training data is different from current retrieval
Training adjusts a model from large collections before a user asks a question. A trained model may reproduce patterns or knowledge without knowing the current source. Retrieval happens for a particular task and can provide fresh material and links. Crawling can support either purpose, depending on the documented crawler and service.
OpenAI distinguishes OAI-SearchBot for discovery from GPTBot controls for potential training. This is why one generic label such as ‘AI crawler’ can produce the wrong policy decision.
Evidence & context: OpenAI Help Center
Retrieval can ground an answer without making it true
Retrieval-augmented generation supplies selected external information to a model. Relevance errors, stale pages, conflicting sources and unsupported synthesis can still occur. The RAG vs AI Agents resource explains how retrieval may also sit inside a broader action loop.
Inspect the cited source and the connection between its evidence and the generated claim. Verification remains necessary when consequences matter.
Evidence & context: Lewis and colleagues, 2020
Design for access, meaning and provenance
- Serve important information in accessible text and stable URLs.
- Use descriptive titles, headings and internal links.
- State who produced the information and what evidence supports it.
- Keep dates, conditions and limitations visible where relevant.
- Use platform controls according to the crawler's documented purpose.
Sources & further reading
- Optimizing your website for generative AI features on Google Search
Google Search Central. Official Google guidance checked 13 September 2026. It treats AEO and GEO work for Google as part of SEO, recommends established search foundations and unique people-first information, and does not describe other assistants' systems.
- AI features and your website
Google Search Central. Official documentation for AI Overviews and AI Mode checked 13 September 2026. Product behavior, availability and reporting can change; eligibility never guarantees crawling, indexing or appearance.
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis and colleagues, 2020. Research combining generation with retrieved material. Retrieval should not be interpreted as proof of factual accuracy.
- Publishers and Developers — FAQ
OpenAI Help Center. Official publisher guidance checked 13 September 2026. It distinguishes OAI-SearchBot discovery from GPTBot training controls and documents ChatGPT referral tagging; crawler roles can change.
Examples and exercises are illustrative unless attributed to a source. No independent expert review is claimed.
A correction, a counterexample or an experience worth sharing?
Join the conversation ↗