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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesStart with the AI task, not a fashionable file format. Define the questions your system must answer, select authoritative pages, remove duplicate URL variants, preserve meaning and provenance, validate every extracted value against its source, and refresh records as they change. Plain text, HTML, Markdown, JSON, PDF and office files can all be appropriate when the destination supports them; no single “AI-ready” format guarantees indexing or citations.
1. Define what the AI workflow must answer
Write the target questions before touching a crawler or parser. A support assistant, a product-search index and a document summarizer need different fields and different tolerances for missing data. Record the intended audience, geographic scope, freshness requirement, acceptable sources and the action taken when evidence conflicts.
Set an explicit source boundary
- List the domains, URL prefixes or record collections that are in scope.
- Exclude faceted navigation, internal search results, print views and tracking-parameter variants unless they contain unique information.
- Decide whether comments, reviews, archived pages and downloadable files are authoritative or supplementary.
- Assign an owner for each source and a date after which a record must be reviewed.
Google Cloud Agent Search documentation recommends specifying URL patterns to include and exclude before indexing. This is a service-specific control, but the principle applies to any ingestion pipeline: deliberate selection is safer than crawling everything.
2. Make pages fetchable and render the same way for machines
Before cleaning content, verify that the crawler used by your destination can reach it. Check robots rules, authentication, firewalls, proxy policies, rate limits, sitemap availability and TLS errors. A page that works in your browser may still be unavailable to an ingestion crawler.
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- Fetch the URL with the same user-agent or connector used in production.
- Compare the raw response with the browser-rendered DOM. Note text that appears only after JavaScript runs.
- Confirm that required API calls are not blocked by robots rules, a firewall or an origin policy.
- Save the response, rendered HTML and retrieval timestamp for a test sample.
Google Search Central says Google can process JavaScript when it is not blocked, while also warning that JavaScript-based SEO is more complex. Google Cloud Agent Search uses its own crawler and separately fetches sitemaps with Googlebot, so its behavior should not be generalized to every product.
Use a browser only when it is necessary
Server-rendered HTML is normally easier to reproduce and validate. If critical facts appear after interaction, document the required clicks, waits and selectors, then test those steps repeatedly. Keep an untouched capture of the source so a cleaned representation can be audited.
3. Canonicalize URLs and remove duplicate documents
Duplicate control is both a quality and a cost measure. Normalize host casing, default ports, trailing-slash rules, fragments and tracking parameters according to your site’s canonical policy. Store the canonical URL alongside the originally observed URL rather than discarding that evidence.
Handle dynamic and alternate URLs
- Exclude internal search URLs and unbounded filter combinations.
- Map print, AMP, mobile and locale variants to a declared canonical record when their content is equivalent.
- Keep genuinely different language, region or product-variant pages as separate records with explicit locale or variant fields.
- Hash normalized content to detect copies that use different URLs.
Google Cloud warns that each unique URL is treated as a separate document; variants can increase storage costs and produce duplicated results. Google Search Central likewise recommends reducing duplicate content. A canonical link element is useful evidence, but your pipeline should still apply its own URL and content checks.
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Clean a page by removing information that is irrelevant to the task, not by stripping everything except paragraphs. Preserve headings, list order, table headers and cells, captions, units, dates, entities and relationships that change interpretation.
Keep structure that answers questions
- Represent a heading hierarchy so a paragraph remains associated with its section.
- Convert tables into records with stable column names; never concatenate cells into an unlabeled sentence.
- Keep list order when it expresses priority or procedure.
- Capture alternate text and figure captions when they carry factual content.
- Separate navigation, cookie notices and repeated footer text from the main article, but retain legal or eligibility text if the task depends on it.
Semantic HTML improves human readability and accessibility, but Google Search Central says perfectly semantic or valid HTML is not required for its systems to understand pages. Treat semantic markup as a clarity aid, not a ranking guarantee.
Compare cleaned output with the source
For every extraction release, sample records and inspect them beside the original page. Check that negations, quantities, dates, table relationships and headings survived. Store an extraction version and parser name so a later correction can be traced to the code that produced it.
5. Choose a consistent representation and schema
Use the format your destination can ingest reliably. Google Cloud Agent Search lists support for TXT, JSON, Markdown, PDF, HTML, DOCX, PPTX, XLSX and XLSM in its unstructured-data ingestion documentation. That list is specific to that service and may change; other systems may accept fewer or more formats.
A practical record shape
{
"id": "product-123",
"canonical_url": "https://example.com/products/123",
"source_url": "https://example.com/products/123",
"title": "Example product",
"locale": "en-US",
"retrieved_at": "2026-09-29T12:00:00Z",
"content": "Cleaned text or structured sections...",
"entities": [{"name": "Example Corp", "type": "Organization"}],
"provenance": {"extractor": "parser-4.2", "source_hash": "..."}
}Use stable field names and types. Keep identifiers immutable, dates in an unambiguous time zone, and units explicit. Retain source URL, retrieval date, locale, parser version and content hash so an answer can be checked against the page that supplied it.
Where JSON-LD helps
JSON-LD contexts map terms to IRIs, allowing systems to interpret shared vocabulary consistently while reshaping variable document data into a more deterministic structure. It is useful when entities and relationships cross documents. It is not mandatory for every AI workflow: a destination may work better with plain text, Markdown, HTML or ordinary JSON.
6. Validate quality, security and ownership
Validation must test both syntax and truth. A file can be valid JSON while containing a copied price, a stale date or a shifted table column.
Automated checks
- Schema and encoding validation, including required fields and allowed types.
- URL status, redirect-chain and canonical checks.
- Duplicate detection by canonical URL and normalized-content hash.
- Range, unit and date checks for numeric and temporal fields.
- Required provenance fields and parser-version checks.
- Secret and personal-data scans before publishing or indexing.
Human review thresholds
Route records to a reviewer when extraction changes a legal term, safety instruction, medical claim, price, eligibility rule or other high-impact fact. The UK Department for Science, Innovation and Technology’s “Making government datasets ready for AI” framework addresses quality, governance, metadata, APIs, human-in-the-loop checks and stewardship. Apply the same principle proportionately: automate routine checks, but define where a person must approve the result.
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Google recommends validating structured data against applicable guidelines and policies. Validation tools can confirm markup shape; they cannot prove that your page says what the record claims.
7. Refresh records and monitor drift
Web data decays. Store the last successful fetch, HTTP status, content hash, extraction outcome and next review time. Refresh more often for prices, availability or policy pages and less often for stable reference material. There is no universal schedule; set it from the change rate and the consequences of stale information.
Alert on meaningful changes
- Canonical URL changes, redirect loops or repeated timeouts.
- Large text or table changes outside an expected publishing window.
- Missing headings, fields or entities that were previously present.
- Robots, firewall or authentication changes that prevent fetching.
- Duplicate counts rising after a site redesign.
Keep the previous approved record until the replacement passes validation. That prevents a transient outage or malformed release from erasing the only usable version.
8. Does AI search need special schema markup?
No special markup is required for Google’s generative AI search features. Google Search Central states: “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 supports appropriate search features, and validate it against Google’s policies. Crawlability, accessible content and ordinary technical quality remain important.
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Do not promise that an AI-specific manifest guarantees inclusion or citation. LLM-LD 1.0, a draft specification published by CAPXEL in February 2026, 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 proposals, not established requirements or an independently verified industry standard.
9. A complete cleaning workflow you can implement
- Write the task contract: questions, sources, locales, freshness and error tolerance.
- Declare URL rules: include and exclude patterns, canonicalization and dynamic-URL policy.
- Run access tests: crawler fetch, JavaScript rendering, sitemap and authentication checks.
- Capture originals: raw response, rendered output, URL and retrieval timestamp.
- Extract structure: headings, lists, tables, entities and task-relevant metadata.
- Normalize records: stable schema, IDs, types, units and provenance.
- Deduplicate: canonical URL and normalized-content checks, with an audit trail.
- Validate: syntax, source truth, security, policy and human-review thresholds.
- Publish to the destination: choose the supported format rather than forcing JSON-LD or any other single format.
- Monitor and refresh: detect drift, preserve the last approved version and re-run all checks.
10. Capturing rendered pages without losing an audit trail
If your source is a JavaScript-heavy page, a screenshot can document what a visitor saw, but it is not a substitute for accessible text extraction. Keep the URL, timestamp and rendered HTML or API response with the image. Use screenshots to review layout, verify consent-state behavior or preserve visual evidence, and use structured extraction for facts that an AI system must search.
Or skip the browser setup
ScreenshotNeo is a website screenshot API and MCP server for developers. It accepts consent banners before capture and removes more than 60 known consent platforms, newsletter popups and chat widgets; each step can be disabled. Bot checks, CAPTCHAs, blank pages, timeouts, failed loads and cache hits cost nothing, and response headers report the page verdict and whether the request was billed. Its MCP tools—take_screenshot, get_page_info and capture_pdf—work with Claude, Cursor and other MCP clients.
One GET request returns PNG, JPEG, WebP or PDF. The API supports full-page and selector captures, lazy-image loading, dark mode, device presets, custom viewports, retina scale, PDF paper and page options, custom CSS and JavaScript, clicks, waits, blocking rules, headers, cookies, user agents, authorization, timezone, geolocation, transparent backgrounds, resizing, chosen-TTL caching, signed links, asynchronous webhooks, bulk capture of 100 URLs per call, a usage API and an OpenAPI specification. Common parameter names used by other screenshot APIs also work.
cURL (see the ScreenshotNeo documentation):
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpPython:
import requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)Node.js:
const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);The Free plan includes 1,000 shots per month with no card. Paid plans start at $5 for 3,000 shots; every feature is on every plan, and yearly billing gives two months free. Create a free ScreenshotNeo account to try the capture step before building it into your pipeline.
11. Troubleshooting checklist
The index contains several copies of one page
Inspect query parameters, fragments, redirects, print or mobile paths and locale rules. Set one canonical ID, exclude unbounded search and filter URLs, and re-run content-hash deduplication.
Important text is missing
Compare raw HTML with rendered DOM and check blocked JavaScript or API requests. Add a supported rendering step, or expose the content in server-rendered HTML. Preserve a sample that demonstrates the failure.
Tables became inaccurate prose
Change the extractor to retain headers, row boundaries, units and footnotes. Reject records when column counts or required labels do not match the source.
Fresh records fail validation
Check redirects, TLS, robots, authentication, rate limits and sitemap access. Keep the last approved record, log the failure and retry according to your source’s change and outage pattern.
Markup validates but answers are wrong
Validation confirms syntax, not factual accuracy. Compare values with the original page, inspect provenance and require human review for high-impact fields.
12. Decision guide: which representation should you choose?
| Need | Practical choice | Reason |
|---|---|---|
| Human-readable passages | Markdown, HTML or TXT | Preserves readable text with low parsing overhead. |
| Stable fields and joins | JSON with a declared schema | Explicit types, identifiers and machine validation. |
| Cross-document entities | JSON-LD or another graph representation | Contexts and IRIs connect shared terms. |
| Scanned or layout-dependent evidence | PDF plus extracted text and provenance | Retains visual record while keeping searchable content. |
| Destination-specific ingestion | The destination’s documented formats | Compatibility beats format fashion. |
Frequently Asked Questions
How do I remove duplicate pages before indexing?
Apply canonical URL rules, exclude dynamic search and filter URLs, normalize variants, and compare normalized-content hashes while retaining the original URL in provenance.
What format should web data be in for an LLM?
Use the format your destination accepts reliably. Consistent JSON is useful for typed records, while Markdown, HTML, TXT or PDF may be better for passages or layout-dependent evidence.
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No. JSON-LD can make terms and relationships more consistent, but no markup format guarantees inclusion, ranking or citation.
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