SkillHack › Career guides › AI Developer Advocate
How to become an AI Developer Advocate
Docs, sample apps, talks, and community feedback loops. Help developers build with AI and carry their voice back to the product.
- 5phases in the roadmap
- 15topics to work through
- 17graded practice questions
- Freeno payment, ever
The AI Developer Advocate roadmap
Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.
Phase 1AI developer advocacy foundations
Developer advocacy
What developer advocacy is: making developers successful and carrying their voice back into the company.
Understanding developers
Developers are not one audience.
The AI dev landscape
Map where your product sits among hosted APIs, open-weight self-hosted models, and orchestration frameworks.
Phase 2AI developer advocacy content
Documentation
Structure docs by user need using Diataxis: tutorials, how-to guides, reference, and explanation.
Sample apps
A good sample teaches one concept with minimal incidental complexity, runs in a few steps, marks illustrative vs production code, and never models unsafe practices like hard-coded secrets.
Tutorials
A tutorial guarantees an early success on a single happy path.
Phase 3AI developer advocacy communication
Talks and demos
Live demos over conference WiFi fail.
Technical writing
Write plainly: active voice, concrete verbs, reader-directed sentences that say what the developer does and gets.
Community building
Steward community culture and safety so beginners are welcome and helpers are supported.
Phase 4AI developer advocacy feedback loop
Gathering developer feedback
Triangulate signals across tweets, tickets, forums, and telemetry to characterize a problem's real scope, and carry the problem (not one loud user's proposed solution) to the team.
Advocating internally
Get fixes prioritized by translating developer pain into telemetry, quotes, business cost, and a concrete sized proposal framed to the owning team's goals, not vague pressure or overreach.
Issue triage
Run a repeatable process: reproduce, categorize, dedupe, redirect questions, and prioritize reproducible high-impact bugs so scarce engineering attention lands where it matters.
Phase 5AI developer advocacy impact
Measuring DevRel
Report an honest funnel of leading and lagging indicators (awareness, sign-ups, activation, retention) plus qualitative signals, explicit about attribution limits, not vanity metrics or over-claimed…
Developer experience
Diagnose where the funnel actually leaks.
Scaling advocacy
Scale through leverage: self-serve assets, community champions, and enablement let developers succeed without a human in the loop, reserving direct time for the highest-impact gaps.
Reading for this path
The primary sources behind the topics above, all free to read.
- developer-relations.com: DevRel guides
- Anthropic developer documentation
- OpenAI platform documentation
- Diataxis documentation framework
- Write the Docs: documentation guides
- Anthropic API quickstart
Coming from another job?
Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.
Other AI career paths
AI Engineer
Prompt and context design, tools, retrieval, orchestration, evaluation, and structured output. Build reliable agent apps end to end.
AI Evals Engineer
Benchmark design, LLM-as-judge, regression suites, and quality metrics. Measure what models actually do before and after every change.
AI for Science Engineer
Modeling in biology, chemistry, physics, and materials, plus research tooling. Apply AI to scientific discovery.
AI Governance Analyst
Regulatory compliance, model cards, risk frameworks, and audit trails. Keep AI systems accountable and inside the rules.
AI Infrastructure Engineer
Data pipelines, infrastructure, deployment, observability, and cost control. Keep data, infra, and models reliable and cost-aware.
AI Integration Engineer
APIs, enterprise systems, legacy migration, and AI-feature rollout. Wire AI capabilities into the software businesses already run.
Start the AI Developer Advocate path for free
The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.
No payment, no credit card, no CV. Sign in with Google.