A documented AI bot robots.txt check is a reproducible record of the exact file you reviewed, the URL path you tested, the 32 product-token results the tool produced, and the verification actions you ran afterward. Because the AI Bot Robots.txt Checker runs entirely in your browser and only sees the text you paste plus the path you enter, the same input always yields the same report — so your notes can act as a fixed audit artifact rather than a vague recollection. Treat the documentation as the canonical answer to three questions: which operators the pasted rules actually target, which paths each operator is allowed or disallowed from fetching, and what you did to confirm the policy before deploying it. Pair the captured result with a fetch of the live /robots.txt and a check against current operator documentation, because robots.txt is voluntary and the checker cannot see what is actually live on your server.
Why a written record of AI crawler robots.txt checks matters
Most teams underestimate how fast an AI crawler access policy drifts. A new dedicated training-control token appears, an operator renames a product, or someone refactors the wildcard group and accidentally changes how a retrieval bot sees a private folder. A dated record of the file, the path, and the 32-token result makes every one of those changes reviewable after the fact.
Documentation also lets multiple roles work from the same evidence. SEO can defend a decision in a planning meeting, engineering can reproduce the result from the saved file, and legal can show intent if a dispute ever surfaces. Because the checker's output is deterministic from pasted text and a path, the saved file plus the saved path is enough to regenerate the report months later without contacting your live server.
Finally, a written record disciplines the review itself. When you know the result will be filed, you are more likely to test several representative paths, to mark which rows matter, and to actually open the operator's current documentation rather than assume the rules from memory.
What your AI crawler robots.txt documentation should contain
Six elements belong in every audit record, and skipping any of them tends to weaken the trail the next reviewer has to work from.
| Documentation field | What to record | Source |
|---|---|---|
| Source file | Exact robots.txt text that was pasted, including comments and line endings | Your production /robots.txt export |
| Test path | Case-sensitive path beginning with a slash, optional query string | Path you modeled |
| 32-token result | Allowed or blocked outcome per row, plus operator, purpose category, and source status | AI Bot Robots.txt Checker output |
| Operator documentation | URLs of operator pages you consulted for critical rows | OpenAI, Anthropic, Cloudflare references |
| Live file confirmation | Scheme, host, status, content type, and a diff against the pasted copy | Direct fetch of /robots.txt |
| Reviewer and timestamp | Name of the person who ran the check and the date | Your audit log |
Storing those six items together turns a one-off check into an artifact that survives team changes and tooling refreshes.
Run the check and document the steps
Use the AI Bot Robots.txt Checker as the canonical review surface, then save every input and output alongside the policy decision it supports.
- Export the production file. Save the exact robots.txt as your server serves it, including comments, and store it in the documentation folder for this audit cycle.
- Open the AI Bot Robots.txt Checker. Paste the copied text into the input box and confirm the file is under the 500 KiB limit and contains no more than 5,000 non-empty access rules.
- Enter the path to model. Type a case-sensitive string beginning with a slash; add a query string only when it matches the path a real crawler would request.
- Run the check and capture the 32-token report. Each row carries the operator name, the purpose category, the source status, and an allowed or blocked result for your path. Save the full table rather than a screenshot of one row.
- Mark the rows that drive the decision. Pay particular attention to dedicated training-control tokens such as Google-Extended or Applebot-Extended, which sit alongside retrieval crawlers and need separate sign-off.
- Cross-check critical rows against operator documentation. The OpenAI publishers FAQ and Anthropic's web crawler controls page should each be linked next to the matching row, with Cloudflare's AI crawler reference covering the long tail of products.
- Fetch the live /robots.txt from the exact scheme and host. Confirm the response returns successfully, the content type is plain text, and the served file matches the pasted copy; record all three facts.
- File the audit artifact. Store the source file, the test path, the captured 32-row table, the operator documentation URLs, the live-fetch result, and the reviewer name and date as a single dated record.
These eight steps extend the checker's own three-line operating procedure and add the five documentation layers that turn a one-time review into a repeatable workflow.
Reading the 32-token result table for your records
The 32 rows in the captured report are robots.txt product tokens, not a promise that every string appears verbatim as an HTTP User-Agent header. That distinction belongs in your notes because it controls how each row should be acted on. Dedicated control tokens such as Google-Extended or Applebot-Extended govern model-training or data-use preferences rather than page retrieval. Crawler or assistant retrieval tokens govern whether the operator fetches a page at all. The table's purpose-category and source-status columns let your record preserve those distinctions instead of treating every row the same way.
Document the matching logic too, so reviewers do not have to re-derive it. RFC 9309 says the longest matching path pattern wins, equally specific Allow rules beat Disallow, the wildcard User-agent: * group only applies when no specific product-token group matches, comments are stripped before interpretation, and an asterisk matches any sequence while a terminal dollar sign anchors to the end of the path. When you write the result into your note, spell out which group owned the path and which rule actually fired.
One subtle case to record explicitly: matching is case-sensitive for paths, so /Private and /private can produce different outcomes. If you only test one casing, mark the second one as untested in the same artifact.
After the check: verification steps to include in your documentation
Operator documentation is the first follow-up. The OpenAI publishers FAQ at OpenAI's publishers and developers page covers GPTBot and the OpenAI crawler family, and Anthropic's web crawler controls page covers ClaudeBot. Cloudflare's AI crawler reference at the Cloudflare AI bot directory and Cloudflare's managed robots.txt page cover the long tail and are listed as the current cross-reference for major products.
Live file confirmation is the second follow-up. The checker does not fetch your website, so it cannot detect redirects, CDN overrides, syntax served only to certain clients, incorrect host scope, caching delays, or an unreachable file. Your note has to capture a fresh fetch, the status code, the content type, and a comparison with the pasted copy. If the served file differs from the pasted copy, the pasted-copy result is moot and the check needs to be re-run from the live text.
Behavioral follow-ups belong in the same record. Robots.txt is voluntary and is not authentication, authorization, a firewall, or proof that content will stay out of an AI model. A crawler can ignore the file, and publicly listing a path can reveal it. Your documentation should name the server-side access controls and log monitoring that back up the published policy, and should point reviewers at the AI bot robots.txt verification workflow for the next step in the audit chain.
Limits to state plainly inside the audit record
A defensible record is one that admits what the tool cannot tell you. The checker follows RFC 9309 core matching only; it does not interpret Sitemap or Crawl-delay lines, even though those lines can be meaningful to specific crawlers. It caps inputs at 500 KiB and 5,000 non-empty access rules and caps aggregate matching work at 20,000,000 operations, so a file inside the byte limit can still be rejected by the safety limit. It processes text only — it does not upload the file, fetch the website, or contact an operator.
An allowed result means only that the pasted rules do not request a block for the selected path under the implemented protocol logic; it does not prove that the operator will crawl, index, cite, train on, or display the page. A blocked result likewise does not prove enforcement or removal from any index. Write those limits into the artifact itself, not into a separate disclaimer, so reviewers who only see the 32-token table do not assume the report covers more than it does.
For a deeper look, see Free llms.txt Generator Without Signup or Crawling.