To prepare web data for AI, first define what the system must answer and which pages it needs. Then check that the intended crawler can access those pages, choose one canonical record per page, extract the content without losing important structure, add consistent fields and source metadata, validate the result, and refresh it when the source changes. The right output format depends on the system receiving the data: there is no single format or special schema that every AI workflow requires.
How do I clean web data for AI? Start with the task
Cleaning is not simply stripping HTML until only plain text remains. A useful AI record should retain the information needed for the intended task, preserve meaningful relationships, identify where it came from, and avoid needless duplicate or stale copies. Begin by writing down the questions the system should answer, then choose the pages or records likely to contain those answers.
Define scope and URL rules
Decide which sections of the site are in scope and which URL patterns to include or exclude. For example, a product knowledge base may need product pages and help articles but not internal search results, filtered listings, or alternate URL forms. Google Cloud Agent Search recommends defining URL patterns for inclusion and exclusion before indexing. Its documentation also warns that it treats each unique URL as a separate document, so URL variants can create duplicate results and increase storage costs. These are details of that service, not universal behavior for every indexer.
Record the intended scope in a configuration rather than relying on ad hoc cleanup later. If URL parameters represent genuinely different content, preserve them; if they only change tracking, sorting, or presentation, decide whether to normalize or exclude them. Confirm the choice against the source pages so distinct records are not accidentally collapsed.
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Check access and rendering in the destination system
Verify the crawler or ingestion method that will actually collect the data. Robots rules, firewalls, proxies, authentication, and sitemap access may affect one system differently from another. Google Cloud Agent Search, for example, uses its own crawler and separately fetches sitemaps with Googlebot; do not assume that one successful fetch proves another system has equivalent access.
For JavaScript-rendered pages, check that the relevant content is available to that crawler and is not blocked. Google Search Central says Google Search can process JavaScript content when it is not blocked, while noting that JavaScript-based SEO can be more complex. That guidance concerns Google Search; test the target ingestion system directly rather than generalizing it to all crawlers.
How do I remove duplicate pages before indexing?
Choose a canonical record for each piece of content and normalize URL variants according to the site and destination system. Common candidates for review include alternate protocols or hostnames, trailing-slash variants, tracking parameters, print views, pagination, and dynamically generated search results. These are examples to inspect, not a universal list of URLs that should always be removed.
- Collect candidate URLs from the source pages, sitemap, or the ingestion system’s discovery output.
- Group URLs that appear to represent the same content. Compare page content and canonical signals rather than assuming similar-looking URLs are duplicates.
- Choose the preferred URL pattern and configure inclusion, exclusion, or normalization in the system that supports it.
- Check the resulting records for both duplicate copies and accidental loss of distinct pages.
- Repeat the check after changing URL rules or refreshing the index.
Google Search Central recommends reducing duplicate content, and Google Cloud Agent Search specifically notes the duplicate-document and storage-cost consequences of unique URL variants in its service. Canonicalization improves a dataset only if the selected record is the right one; removing a meaningful locale, version, or page variant can be as damaging as keeping unnecessary copies.
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Extract the main information and the structure that carries meaning. Depending on the task, that may include page title, headings, lists, table headers and values, names of entities, and relationships between facts. A flattened text dump can be adequate for some use cases, but it can make a table’s labels or the association between a heading and its section ambiguous.
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Remove boilerplate selectively
Navigation, repeated footers, cookie notices, and unrelated promotional material may be noise for a particular task. Remove them only when they are not part of the information being sought. A navigation label can be useful context for a site map; a policy notice can be the target content in a compliance workflow. Compare extracted output with the original page before treating the cleaned version as trustworthy.
Semantic HTML can make pages easier for people and assistive technologies to read, but it is not a prerequisite for every system to understand them. Google Search Central advises focusing on human readability rather than perfect semantic code. Preserve useful structure in your own representation when it helps the task; do not mistake markup neatness for factual accuracy.
What format should web data be in for an LLM?
Use the format accepted by the destination, and make the records consistent within that format. Google Cloud Agent Search’s unstructured-data ingestion documentation lists TXT, JSON, Markdown, PDF, HTML, DOCX, PPTX, XLSX, and XLSM. That list describes that product’s documented support, not a universal LLM requirement. Other ingestion pipelines may expect a different representation.
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For records you control, stable field names, types, and identifiers make processing more predictable. Retain provenance such as the source URL and retrieval date so a reviewer can find the original and determine whether a record is stale. A compact JSON record might look like this:
{
"id": "https://example.com/help/account",
"source_url": "https://example.com/help/account",
"retrieved_at": "2026-09-29",
"title": "Account help",
"content": "The extracted main content goes here.",
"sections": [
{"heading": "Sign in", "text": "Section text goes here."}
]
}This is an illustrative shape, not a mandated schema. Choose field types and required fields based on the system consuming the data. If the destination accepts only plain text or another format, keep equivalent provenance in whatever metadata mechanism it provides.
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When JSON-LD helps—and when it does not
JSON-LD uses contexts to map terms to IRIs, helping systems interpret shared terms consistently; it can also reshape variable document data into a more deterministic structure. Use it when the data and destination benefit from those semantics, not simply because the letters “AI” appear in the project description. The JSON-LD 1.1 specification is maintained by the JSON for Linking Data Community Group; check the relevant publication and implementation status before making conformance claims.
Do not confuse a structured representation for your own pipeline with a requirement imposed on public web pages. Google Search Central’s current guidance, reviewed September 29, 2026, says: “Structured data isn’t required for generative AI search, and there’s no special schema.org markup you need to add.” Continue using structured data when it accurately describes a page and serves an appropriate use; validate it against applicable guidance.
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No special schema markup is established as a general requirement for AI search. Google’s guidance for its generative AI search features emphasizes publicly accessible, crawlable pages and established technical practices. It recommends sound technical structure, avoiding blocked JavaScript content, and reducing duplicate content; it does not promise that a particular markup file will secure inclusion or citation.
LLM-LD is a draft proposal from CAPXEL, which the specification says was published in February 2026. It proposes crawl-ready, ingest-ready, and agent-ready levels and files such as robots.txt, sitemap.xml, Schema.org JSON-LD, and llm-index.json. Treat those as the proposal’s approach, not as a general standard or a requirement adopted by AI search systems. Claims in that specification about adoption or directories should be understood as maintainer claims, not independently established coverage.
How should you validate and govern cleaned data?
Validation should check both the record’s structure and whether its claims match the source. A syntactically valid JSON file can still contain truncated text, incorrect table associations, stale facts, or a wrong source URL. Google’s structured-data guidance recommends validation against applicable guidelines and policies. The UK Department for Science, Innovation and Technology’s AI-ready public-sector data framework, published January 19, 2026, addresses quality, governance, metadata, APIs, human-in-the-loop checks, and stewardship.
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- Accuracy: sample extracted claims and compare them with the original page.
- Completeness: check that expected sections, records, and fields are present.
- Consistency: check field names, types, identifiers, and date formats across records.
- Provenance: retain enough source and retrieval information to trace and recheck a record.
- Security and ownership: establish who may access, change, and approve the data, and where human review is required.
- Destination compatibility: test representative records in the actual ingestion system, not only in a local parser.
Automated checks are useful for repeatable conditions such as missing fields or malformed syntax. Human review is especially important where an extraction error could materially mislead users or affect a consequential decision. Assign an owner for the source and a review path for issues; a clean-looking export is not a substitute for stewardship.
How often should web data be refreshed?
Refresh records according to how quickly the underlying source changes and how costly stale information would be for the task. Re-fetch or refresh pages, detect broken or stale records, and repeat duplicate and quality checks. The cited guidance does not prescribe one refresh interval for all sites or use cases, so set and document a schedule appropriate to the content rather than adopting an unsupported universal cadence.
How to choose an extraction and cleaning approach
There is no single technique that wins for every corpus. Compare candidate approaches against the properties that matter to the destination:
| Decision axis | What to check |
|---|---|
| Accuracy | Do extracted values match the source page? |
| Structure | Are table labels, sections, and relationships preserved where needed? |
| URL handling | Can the approach avoid unwanted duplicate and dynamic URL variants without discarding distinct pages? |
| Metadata and updates | Can it retain provenance and support tracking source changes? |
| Validation effort | How much automated checking and human review does it take to trust the output? |
| Compatibility | Does the destination accept the resulting format and fields? |
These are practical decision axes synthesized from the cited guidance, not a published benchmark. Test a representative set of difficult pages—including dynamic content, tables, and duplicate URL forms—before processing a large corpus.
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Common cleaning and ingestion problems
Pages are missing from the corpus
Check whether the target crawler can reach them and whether robots rules, a firewall, proxy, sitemap access, or JavaScript rendering is involved. Test with the destination system’s own documented behavior; access by a browser or a different crawler is not proof of equivalent access.
The index contains near-identical copies
Compare the URLs and content, then review your include, exclude, and canonicalization rules. Dynamic search results and alternate URL forms may be creating distinct records. Avoid blanket normalization until you confirm that locale, version, or other variants are not meaningful.
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Inspect the original and extracted output side by side. If labels and values became detached, retain table structure or transform it into explicit labeled fields. Test representative tables rather than assuming a successful text extraction preserved their relationships.
The markup validates but the answer is wrong
Syntax validation cannot prove that extracted facts are true or complete. Trace the record to its source URL and retrieval date, compare the value against the page, and route consequential errors for human review.
A proposed AI-specific file does not change visibility
Do not treat a draft manifest or markup proposal as a guarantee of indexing, answer inclusion, or citation. For Google generative AI search, follow its established crawlability and content guidance; other destinations may have their own ingestion requirements.
FAQ
Can plain text be enough for an LLM workflow?
Yes, if the receiving system accepts it and the task does not depend on structure that plain text would lose. Preserve provenance and any needed labels or relationships in the format your pipeline supports.
Does valid HTML guarantee a page will be understood?
No. Google Search Central says perfect semantic or valid HTML is not required for its systems to understand pages; readable structure is still useful to people and may help your own extraction process.
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