16 DEVREL IN THE AI ERA ✣

Generative Engine Optimisation (GEO) and Answer Engine Optimisation (AEO) for DevRel.

If SEO was the discipline of being found by search engines, GEO and AEO are the disciplines of being cited by AI assistants. The two terms overlap in practice but differ in nuance:

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If SEO was the discipline of being found by search engines, GEO and AEO are the disciplines of being cited by AI assistants. The two terms overlap in practice but differ in nuance:

Generative Engine Optimisation (GEO). Optimising for citation and inclusion when generative AI systems (ChatGPT, Claude, Perplexity, Gemini) produce answers. The headline metric is Share of Model or Share of Voice — how often your brand is mentioned in responses to category-relevant prompts.
Answer Engine Optimisation (AEO). Structuring content so AI answer engines can extract clean, direct answers. Overlaps heavily with classical SEO best practices (Q&A formatting, schema markup) but tunes them for conversational extraction rather than ten-blue-links ranking.

For developer products, both matter. The discipline is still young, the data is noisy, but the practices that emerged through 2025–2026 are concrete enough to be worth doing.

Where AI assistants get their answers from

A practical breakdown:

Assistant	Primary index / retrieval source	Notes
ChatGPT	Bing (via OAI-SearchBot for live retrieval); training data	Bing Webmaster Tools verification matters; allow `OAI-SearchBot` separately from `GPTBot` (training-data crawler)
Perplexity	Live web retrieval (PerplexityBot); curated high-trust domains	Citations shown inline; surfaces only sites it considers high-authority
Gemini	Google Search index + Google’s broader retrieval	Strong SEO is prerequisite; YouTube videos with `VideoObject` schema often cited for “how-to” queries
Claude	Live web retrieval (ClaudeBot, Claude-User); training data	Anthropic has not published detailed retrieval mechanics
Copilot (Microsoft)	Bing; specific developer-tool data	DevTools-specific context

DevRel teams optimising for AI visibility need to satisfy multiple systems, each with slightly different mechanics. There is no single GEO playbook that works equally well across all of them.

Foundational GEO practices

These work across most assistants:

1. Authoritative, substantive content

AI assistants weight authority strongly. Authority signals include:

Substantive, original content (engineering posts that actually say something).
Citations from other authoritative sources.
Long-running domain history.
Wikipedia presence (if applicable).
Mentions in trusted publications.

What doesn’t work: shallow content optimised purely for keyword stuffing. AI assistants discount this aggressively.

2. Clear semantic structure

Headings as questions (“How do I X?”) or canonical topic titles.
One concept per page.
Lead with the answer in the opening paragraph.
Bullet-list and table formats where appropriate.

3. Schema.org / JSON-LD markup

For developer-product content particularly useful:

Article with dateModified, author, publisher.
HowTo for tutorials.
FAQPage for FAQ-style content.
SoftwareApplication for product pages.
Organization and Product schema for brand consistency.
VideoObject for embedded videos.

Schema feeds AI crawlers structured data directly, bypassing HTML-parsing ambiguity.

4. Crawler permissions

Audit your robots.txt to ensure AI crawlers can read what you want them to read. See ./documentation-for-agents.md for specifics. Many teams accidentally block AI crawlers while trying to block aggressive SEO scrapers.

5. Cross-platform consistency

Your product’s description on your homepage, your docs site, your README, your Wikipedia entry, and on third-party listings should be consistent. When AI assistants encounter contradictions, they tend to flag uncertainty or pick less authoritative sources.

6. Freshness signals

dateModified updated when content is updated. Versions referenced explicitly. Deprecated content marked as such or removed. AI assistants trained more recently or that retrieve live weight fresh content more heavily.

Developer-product-specific tactics

Beyond general GEO, several practices that matter especially for DevRel:

Maintain a canonical “What is [product]?” page

A short, factual, well-structured page that answers the basic question. AI assistants extract from this page heavily when asked about your product. Don’t bury this content inside a marketing-driven homepage; have a clean answer page.

Maintain “X vs Y” comparison content

Engineering-honest, fact-led comparison content for the top three or four competitors in your category. AI assistants are asked “what’s the difference between X and Y?” constantly, and they cite whoever has the best comparison content.

The trick is honesty. AI assistants discount obviously biased comparison content. Stripe-style fair comparisons get cited; product-pitch comparisons get ignored.

Cultivate third-party authority

Get mentioned positively on:

Reddit (relevant subreddits — weighted heavily by some AI assistants).
Hacker News (substantive Show HN launches and threads).
dev.to and Hashnode posts by independent developers.
YouTube tutorials by trusted educators.
Podcasts with transcripts.
High-authority newsletter mentions (Console.dev, TLDR, etc.).
Stack Overflow answers (the archive is still cited despite the platform’s decline).

These are the second-order authority signals AI assistants weight. DevRel teams that cultivate organic mentions across these channels see better AI-citation performance than teams that rely only on owned content.

YouTube transcripts are heavily used by Gemini (and indirectly by other assistants via training data). A well-produced YouTube tutorial with a thorough transcript can produce more AI-mediated visibility than a blog post on the same topic.

The trade-off: video is expensive to produce. The DevRel teams winning at GEO in 2026 invest in video as a deliberate AI-visibility strategy, not just as a brand exercise.

Publish on platforms AI assistants trust

Your own domain is good but not enough. Cross-publish (with canonical URLs) to:

dev.to and Hashnode.
LinkedIn long-form posts and newsletters.
Substack or Beehiiv.
Medium (less weighted than 2018–2020 but still cited).
Industry publications (TheNewStack, InfoQ, occasional Smashing Magazine, etc.) where the editors will accept submissions.

Tools for monitoring brand presence in AI

A new tools category emerged in 2024–2026. Among the better-known products:

Profound (tryprofound.com). Measures brand visibility across major AI assistants; tracks Share of Model and competitive positioning.
AthenaHQ. AI-search analytics.
Otterly.AI. Similar tracking of AI-mention frequency.
Peec AI. Tracks brand mentions across AI engines.
LLMrefs. Citation and authority tracking.
Goodie tools. GEO-focused stack.
Sight AI (trysight.ai). AI search visibility tracking.
Pure Visibility. GEO platform.

The category is young, the methodologies vary, and the tools sometimes report wildly different numbers. Best use is for relative tracking (am I getting more or less visible over time?) rather than absolute measurement.

Reasonable metrics for DevRel teams

Among the metrics worth tracking:

Share of Voice / Share of Model. Across a defined set of category-relevant prompts, what percentage of responses mention your brand?
Citation accuracy. When AI assistants describe your product, what fraction of technical claims they make are correct?
AI-mediated signup attribution. When you ask new signups how they heard about you, what share say “ChatGPT,” “Claude,” “Perplexity,” or “an AI assistant”?
Authority-domain presence. Are you mentioned on Reddit, HN, GitHub READMEs, prominent YouTube channels?
Citation variance. AI citations fluctuate; high variance month-to-month suggests fragile positioning, while stable citations suggest durable authority.

Caveats

Two important honest acknowledgements:

AI citations are noisy. Multiple analyses through 2026 report 40–60% monthly variance in AI citations across the same prompts. AI assistant models update frequently; context windows shift; retrieval algorithms change. Treat your GEO position as a trend, not a fixed measurement.
AI assistants do not always cite their sources. ChatGPT often answers without showing links. Your AI-mediated influence on developers may be substantial and invisible. Measure what you can; don’t pretend the unmeasurable is zero.

What to deprioritise

A few practices that wasted DevRel teams’ time in 2024–2025 and don’t appear worth the effort in 2026:

Stuffing keywords into AI-readable surfaces. AI assistants discount this.
Generating large volumes of AI-written content optimised for “AI search.” This often hurts authority because AI assistants weight original human-authored content.
Treating llms.txt as a complete GEO strategy. It’s one signal, not the strategy.
Optimising only for ChatGPT. Each assistant has different retrieval mechanics; over-specialisation on one creates fragility.

A 90-day GEO program for a DevRel team

Months 1: Baseline. Run prompts across all major assistants. Audit robots.txt. Run a schema.org audit. Verify Bing Webmaster Tools setup. Identify three to five high-value pages on your domain that need rewriting.

Month 2: Optimise. Rewrite the high-value pages with the practices above. Publish canonical “what is X?” and “X vs Y” pages. Coordinate one major YouTube collaboration with a trusted educator. Ensure all SDK READMEs have date / version / canonical URLs.

Month 3: Measure and iterate. Re-run prompts. Look for delta. Treat as ongoing — month 3 baseline becomes month 6 review.