Technical SEO Prompts: Growth Playbook with Checklists

Technical SEO is infrastructure for both Googlebot and AI assistants.

Random prompts won’t fix crawl waste or schema failures.

In this guide you will learn a structured prompt system for discovery, diagnosis, specs, QA, and monitoring tied to real data (logs, crawls, CWV, AI citations).

Keep this aligned with our prompt engineering pillar at Prompt Engineering SEO so teams ship safe, verifiable outputs.

Prompt system overview

• Inputs first: crawl exports, Search Console, GA4, logs, CWV, schema validation, AI citation logs.

• Stages: discovery → clustering → prioritization → specification → QA → monitoring.

• Guardrails: no code deploys without human review; forbid PII; never fabricate data.

• Outputs: tables with issues, severity, impact, and next steps; specs formatted for tickets.

• Owners: assign who reviews prompts/outputs (SEO lead, dev, legal) and when.

Guardrails to include in every technical prompt

• “Do not invent URLs, data, or code; only use provided inputs.”

• “Flag PII; do not expose user data; anonymize if present.”

• “Recommend validation steps (Rich Results Test, Lighthouse, Playwright, crawlers).”

• “Provide risk, effort, and impact estimates; no promises of ranking.”

• “Format for tickets: issue, evidence, recommendation, validation, owner.”

Discovery prompts (inputs: crawl/SC/logs)

• “From this crawl export, list top issues by count and affected templates; include sample URLs and severity.”

• “Cluster these GSC queries/URLs by template and intent; surface pages with high impressions and low CTR.”

• “From this log sample, identify crawl waste (4xx/redirect chains/parameters) and propose robots/canonical fixes.”

• “List JS-rendering risks from this render-depth report; note missing content or schema post-render.”

Core Web Vitals prompts (inputs: CWV/CrUX/Lighthouse)

• “Summarize CWV issues by template/device; list LCP elements and INP offenders; suggest fixes with effort estimates.”

• “Given this Lighthouse report, draft a ticket-ready summary with actions to reduce INP and CLS.”

• “Propose image optimization steps from this asset list (format, size, preload candidates).”

• “Given CWV data by template, prioritize fixes by traffic x severity; output ticket summaries.”

• “Identify render-blocking scripts/styles from this waterfall; suggest deferral or removal.”

Schema and entity prompts (inputs: schema dumps/validators)

• “Review this JSON-LD sample; list errors, missing required fields, and @id conflicts; provide corrected snippet.”

• “Align schema about/mentions with these entities: [list]; propose updates for Article + Person + Organization.”

• “Suggest Speakable/FAQ/HowTo additions for this template; include validation steps and safety for YMYL.”

• “Generate a schema rollout checklist per template with required fields, @id plan, and parity checks.”

• “Map sameAs updates for Organization/Person when new PR hits; ensure stable @id and canonical URLs.”

• “Flag schema duplication from plugins; recommend consolidation plan.”

Internal linking and crawlability prompts

• “From this crawl, list orphan pages and pages deeper than 3 clicks; suggest pillar/support links and anchors.”

• “Analyze internal anchor text to [pillar]; propose 10 anchor variants and link placements on top supports.”

• “Recommend robots/meta/canonical rules for these parameterized URLs; include examples.”

• “Draft sitemap priorities: which sitemaps to split/merge; update frequency per template.”

• “Detect infinite crawl traps in faceted navigation; suggest noindex/robots/canonical rules.”

• “Suggest TOC and breadcrumb improvements for this template; output anchor targets.”

Log analysis prompts

• “Identify bots spending >X% hits on non-indexable URLs; propose mitigation.”

• “Find long redirect chains from logs; list sources and destinations; propose fixes.”

• “Spot spikes in 5xx/4xx; suggest root-cause hypotheses and monitoring steps.”

• “Detect slow response patterns by path; recommend caching or infra fixes.”

• “List top 100 requested 404s and best redirect targets; avoid loops.”

Edge and headless prompts

• “Propose CDN/worker rules to cache HTML for [templates] while respecting auth and cookies.”

• “Draft header rules for canonicals/hreflang/security headers on a headless setup.”

• “Suggest pre-rendering or SSR options for these JS-heavy pages; list validation steps.”

• “Generate transform rules for link rewriting on edge to fix legacy links; include tests.”

• “Recommend CSP and security headers; note impact on third-party scripts.”

International and multi-domain prompts

• “Audit hreflang from this export; list invalid/ missing pairs and canonicals; propose fixes.”

• “For multi-domain setup, suggest canonical and hreflang strategies; include edge rules if needed.”

• “Localize robots and sitemaps for these markets; ensure no cross-market blocking.”

• “Detect mixed-language pages and wrong-language snippets; propose fixes in meta, hreflang, and schema.”

• “Create redirect and canonical plan for locale migrations (ccTLD → subfolder).”

AI search and answer-engine prompts

• “Using this AI citation log, list pages cited vs ignored; propose technical fixes (schema, render, anchors) to improve citations.”

• “Draft a checklist to ensure pages are extractable for AI Overviews: fast LCP/INP, clear answers, stable schema.”

• “Recommend telemetry to monitor AI answer traffic (logs, prompt tests, screenshot captures).”

• “Suggest changes to make featured answers more extractable (short paragraphs, lists, captions).”

• “Map entities missing from AI citations; propose about/mentions and anchor updates.”

Spec-writing prompts (ticket-ready)

• “Create a JIRA-ready ticket for fixing [issue] with sections: Summary, Evidence (URLs/data), Recommendation, Risks, Validation steps, Owner, Estimate.”

• “Write acceptance criteria for schema rollout on [template], including rendered validation.”

• “Draft test plan for robots.txt change; include staging tests, roll-back steps, and monitoring.”

• “Create migration checklist for [change] with pre/post checks, redirects, and rollback triggers.”

QA prompts

• “Given this deployed change, confirm schema renders via Playwright; list missing fields.”

• “Check production vs staging for [URL list]; flag differences in canonicals/hreflang/meta robots.”

• “Validate internal links after migration; list broken/redirected links and pages with <3 links.”

• “Summarize CWV pre/post change; note improvements or regressions.”

• “Verify sitemap counts vs indexed pages; flag gaps.”

• “Run accessibility spot checks (heading order, alt text) post-template change.”

Monitoring prompts

• “Set weekly checks: sitemap freshness, crawl errors, 5xx spikes, schema errors, AI citation changes; output as a checklist.”

• “Build a dashboard outline combining GSC, logs, CWV, and AI citations; list charts and thresholds.”

• “Propose alert rules for LCP/INP regressions and AI citation drops on key clusters.”

• “Design a prompt to summarize log anomalies weekly with counts and suggested owners.”

• “Generate a monthly health report template with sections for crawl, CWV, schema, AI citations, and incidents.”

Programmatic workflows

• “Given CSV columns [URL, template, issue, evidence], generate grouped summaries and action items.”

• “Create regex or pattern rules to clean URLs with tracking params; propose canonical/robots rules.”

• “Generate internal link suggestions at scale from this entity/tag map; output anchors and target URLs.”

• “Draft edge rules for language or device redirects based on provided mapping; include tests.”

• “Summarize 10k-row crawl to top 10 issues with counts, sample URLs, and impact notes.”

Security and compliance prompts

• “Ensure prompts never output PII; if detected, redact and flag.”

• “List GDPR/consent implications for new tracking or rendering scripts; propose compliant alternatives.”

• “For YMYL, enforce neutral language and include reviewer requirements in specs.”

• “Flag third-party scripts that set cookies without consent; propose mitigations.”

• “Highlight any prompts requesting credentials or sensitive data; block and log.”

Case snippets

• Ecommerce: Prompt-led crawl clustering cut crawl waste 35% and improved LCP/INP via ticket-ready specs; AI citations rose for comparison pages.

• SaaS: Schema prompts fixed @id conflicts and added Speakable/FAQ; AI Overview citations increased and CTR improved 8%.

• News: Hreflang and render QA prompts reduced wrong-language snippets; AI citations stabilized across markets.

• Marketplace: Log prompts identified crawl traps; robots/canonicals reduced wasted hits and improved crawl efficiency 25%.

• Local services: Internal link prompts lifted depth issues; AI answers began citing local pages correctly.

30-60-90 day rollout

• 30 days: build prompt library by task/data source; pilot on one template; set logging and guardrails.

• 60 days: add spec and QA prompts; integrate with tickets; validate outputs with crawlers/validators; start AI citation tracking.

• 90 days: scale to all templates, add log/edge prompts, automate prompt logs, and bake prompts into standard operating procedures.

• Quarterly: regression-test core prompts after model changes; refresh guardrails and training.

Tool stack

• Crawlers (Screaming Frog/Sitebulb) for exports, depth, and schema extraction.

• Log sampling tools; BigQuery or warehouse for joins across logs, GSC, and CWV.

• Render testing (Playwright) for JS and schema presence; Lighthouse for CWV.

• Validators: Schema Markup Validator, Rich Results Test.

• Dashboards: Looker Studio/Power BI with GSC, GA4, CWV, logs, and AI citation data.

• Ticketing: JIRA/Asana with prompt-linked specs; CI for linting schema/templates.

Ops cadence

• Weekly: log and crawl reviews; prompt-driven summaries; check alerts for CWV, 5xx, schema errors.

• Biweekly: ship prioritized fixes; validate renders; update prompt library with findings.

• Monthly: AI citation review, hreflang and sitemap audits, and CWV trend review.

• Quarterly: deeper audits, edge/CDN review, migration dry runs, and model/prompt regression tests.

KPIs and diagnostics

• Crawl waste % (non-indexable hits), depth, orphan count.

• CWV pass rates by template; LCP/INP/CLS trends.

• Schema validation pass rate and @id duplication count.

• AI citation share by cluster; extractability issues reported by prompts.

• Time-to-ticket and time-to-fix for top issues; QA fail rate post-release.

• Migration success: redirect success rate, 404/5xx deltas, and traffic retention.

• Incidents per quarter and time-to-detection; guardrail updates applied.

Prompt library and governance

• Store prompts with fields: task, data source, input format, guardrails, model/version, sample inputs/outputs, owner, risk level, and validation steps.

• Keep red-flag prompts (hallucinations, bad advice) blocked; note reasons.

• Version prompts after model changes; retest “gold” prompts quarterly.

• Assign owners per domain (logs, CWV, schema, international, edge) to keep libraries updated.

• Link prompts to SOPs and ticket templates so outputs drop into workflows.

Role-based usage

• SEO lead: discovery, prioritization, AI citation analysis, and specification prompts.

• Developer/engineer: edge/header prompts, SSR/JS prompts, and validation prompts.

• Analyst: log and crawl clustering prompts; dashboard outline prompts.

• Localization lead: hreflang and mixed-language detection prompts.

• Product/PM: ticket-ready summaries, impact/effort prompts, and rollout checklists.

Example SOP insert

• Step 1: run crawl/log prompts; paste exports.

• Step 2: run clustering prompt; capture top issues with counts.

• Step 3: run spec prompt for top 3 issues; paste into tickets with validation steps.

• Step 4: after release, run QA prompts (render, schema, links, CWV) and log results.

• Step 5: update monitoring prompts and dashboards; log outcomes and learnings.

Reporting cadence

• Weekly: health snapshot (crawl errors, 5xx, CWV, schema errors, AI citations) with top 5 actions.

• Monthly: deep dive on clusters/templates; note fixes shipped and performance changes.

• Quarterly: strategic review of crawl budget, render performance, international setup, and AI extractability; refresh prompt library.

Common mistakes to avoid

• Feeding models incomplete data; outputs become guesswork.

• Skipping validation; trusting AI code/snippets without tests.

• Ignoring PII in logs; risking compliance violations.

• Over-automating internal links or canonicals without human review; causing chaos.

• Using the same @id across entities; fragmenting schema and confusing AI.

• Not tracking model versions; regressions creep in unnoticed.

How AISO Hub can help

• AISO Audit: We review your tech stack, data, and prompts, then deliver a prioritized prompt library.

• AISO Foundation: We build prompt systems, guardrails, and validation workflows so every rec lands cleanly.

• AISO Optimize: We run prompt-driven diagnostics, specs, and QA to improve crawlability, CWV, and AI visibility.

• AISO Monitor: We track schema, CWV, logs, and AI citations, alerting you before technical debt piles up.

Conclusion: prompts are your technical playbook

Technical SEO prompts work when they start from real data, include guardrails, and end with validation.

Use this system to discover, spec, and monitor changes that help Google and AI assistants parse your site.

Keep your library current and tied to the prompt engineering pillar at Prompt Engineering SEO so you ship fast without breaking trust.

Document wins and incidents, and keep refining prompts as your stack and models evolve.