AEO/GEO for Dev Tool Startups: How to Get Your API Cited by ChatGPT

A practical guide for developer-focused companies, anchored in evidence from "How People Use ChatGPT" and a single use case: a payment processing API.

If you're building a dev tool—an API, SDK, or platform for developers—you already care about docs, tutorials, and SEO. You should also care about AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization): making your content easy for ChatGPT and similar LLMs to retrieve, summarise, and cite when developers ask questions. When a developer asks "How do I handle idempotency with a payment API?" or "What's the difference between Stripe and Payway?", you want the answer to point to your docs, not a competitor's or a generic blog.

This post is for dev tool startups. We'll use one concrete example throughout: Payway, a hypothetical payment processing API positioned as a Stripe alternative. The process and principles apply to any API or dev tool; the examples are built so you can swap in your own product and category.

We'll anchor everything in findings from the NBER paper "How People Use ChatGPT": how developers actually use ChatGPT at work, which intents dominate, and what that implies for the content you produce.

How developers use ChatGPT: what the data says

The paper breaks usage into three main buckets: Practical Guidance, Seeking Information, and Writing. For computer-related occupations, the mix looks different from the average:

• Technical Help (programming, APIs, data, math) is 37% of work-related messages—by far the highest share of any occupation group. So developers use ChatGPT heavily for technical tasks: "How do I…?", "What does this error mean?", "How do I integrate X?"
• Asking (information or advice to inform a decision) is 47% of work-related messages for users in computer-related roles, versus ~32% for non-professional occupations. Developers are often asking for understanding, comparisons, or guidance—not only "write this code."
• Getting Information and Making Decisions and Solving Problems are among the top work activities across occupations, including technical ones. So developers are looking for facts (API behaviour, pricing, compliance) and decision support (which API? which approach?).

Implications for a dev tool like Payway:

1. Seeking Information → Developers ask factual questions: "What is an idempotency key?", "What are Payway's rate limits?", "Stripe vs Payway pricing?" Content that gives one clear, factual answer per question is what retrieval and citation favour.
2. Practical Guidance → Developers ask how-to questions: "How do I integrate Payway with Next.js?", "How do I verify webhook signatures?" Content that gives stepwise, copy-pasteable guidance is what gets synthesised and cited.
3. Writing / Technical Help → In a dev context, "Writing" often means code: "Fix this snippet", "Add error handling", "Translate this to Python." Content that provides canonical examples, error references, and patterns is what models reuse when they generate or modify code.

So your AEO/GEO strategy for a dev tool should explicitly target factual reference (Seeking Information), integration and how-to (Practical Guidance), and code and errors (Technical Help / Writing). The process below does that in a repeatable way.

A process for dev tool AEO/GEO

Use a simple four-step loop: map → discover → create → structure. We'll go through each step with Payway as the running example.

Step 1: Map your content to the three use cases

Don't treat "docs" and "blog" as one blob. Map them to Seeking Information, Practical Guidance, and Technical Help / Writing so you know what to optimise for retrieval vs. for step-by-step use.

Seeking Information (factual, "same for everyone")

• Purpose: Answer factual questions developers ask the same way: definitions, specs, pricing, comparisons, compliance.
• Format: Reference pages: one topic per page, clear definitions, tables, numbers.
• Payway examples:
  • "What is an idempotency key in payment APIs?"
  • "Payway API rate limits and quotas"
  • "Stripe vs Payway: pricing, features, and use cases"
  • "PCI DSS requirements when using a payment API"

Practical Guidance (how-to, tutorials)

• Purpose: Answer "How do I…?" with steps and context. The paper shows this is a large share of usage; developers often want to understand and implement, not only copy code.
• Format: Guides and tutorials: prerequisites, steps, code snippets, troubleshooting.
• Payway examples:
  • "How to integrate Payway with Next.js (App Router)"
  • "How to verify Payway webhook signatures"
  • "How to implement retry logic for failed charges"
  • "How to handle 3DS with Payway"

Technical Help / Writing (code and errors)

• Purpose: Support "Doing" and "Writing" in a dev context: code generation, editing, debugging. The paper shows that a big share of Writing is modifying user-provided content; in dev, that's often code. Provide patterns and references the model can reuse.
• Format: Code examples, error code reference, SDK snippets, "before/after" or "wrong vs. right" examples.
• Payway examples:
  • "Payway API error codes and how to handle them"
  • "Create a charge (cURL, Node, Python)"
  • "Idempotency: example request and response"
  • "Webhook payload example and signature verification code"

Practical takeaway for Payway: Maintain three content "lanes": (1) Reference for facts and comparisons, (2) Guides for integration and how-to, (3) Code/errors for examples and error handling. Each lane serves a different way developers use ChatGPT.

Step 2: Discover the query clusters that matter for your category

You're not optimising for "payment API" in the abstract. You're optimising for concrete questions developers ask when they're evaluating, integrating, or debugging. For a payment API (Stripe alternative), clusters typically look like this:

Evaluation / comparison

• "Stripe vs [YourAPI]"
• "Best Stripe alternative for [use case]"
• "[YourAPI] pricing"
• "Does [YourAPI] support [feature]?"

Concepts and specs

• "What is idempotency in payments?"
• "What are payment API rate limits?"
• "PCI DSS and payment APIs"

Integration

• "How to integrate [YourAPI] with [framework]"
• "How to handle webhooks [YourAPI]"
• "How to implement 3DS / SCA with [YourAPI]"

Errors and debugging

• "[YourAPI] error code [X]"
• "How to handle failed charges / retries"
• "Webhook signature verification [YourAPI]"

Payway example: For "How to verify Payway webhook signatures", you're targeting both the Seeking Information intent ("What is webhook verification?") and the Practical Guidance intent ("How do I do it with Payway?"). So you need (a) a short, factual explanation and (b) a step-by-step section with code. One page can serve both; use clear headings so a model can pull the right block for the user's exact question.

Step 3: Create one canonical asset per query cluster

The paper stresses that one clear, canonical answer per question is easier for retrieval and citation than the same information scattered across many pages or phrased differently each time. For each important query cluster, assign one primary URL (and one primary section if the page covers multiple questions).

Use question-shaped headings, front-load the answer, use lists and tables for facts, and keep code blocks canonical and copy-pasteable. One primary concept or question per section helps retrieval match the user's exact question.

Step 4: Structure each asset for retrieval and citation

Structure your pages so that both search/retrieval and the model can quickly find and quote the right part. Use question-shaped headings, front-load the direct answer in the first paragraph, use tables for specs and comparisons, and keep one clear example per use case.

Summary

• Evidence: Developers use ChatGPT heavily for Technical Help (37% of work messages in computer-related roles), Asking (47%), and Getting Information / Making Decisions. Your content should serve factual lookups, how-to guidance, and code/error reference.
• Process: Map content to Seeking Information, Practical Guidance, and Technical Help → Discover query clusters → Create one canonical page per cluster → Structure with question-shaped headings, front-loaded answers, tables, and canonical code.
• Payway: Treat the payment API as the anchor: comparison and concept pages for Seeking Information, integration and verification guides for Practical Guidance, error reference and code examples for Technical Help.

AEO/GEO for dev tools isn't about gaming algorithms—it's about being the obvious, canonical source for the questions your audience already asks in ChatGPT. Do that consistently, and you increase the likelihood that the model retrieves and cites your docs when it matters.

Based on: Chatterji, A., Cunningham, T., Deming, D. J., Hitzig, Z., Ong, C., Shan, C. Y., & Wadman, K. (2025). "How People Use ChatGPT." NBER Working Paper 34255.