Course Overview: What You’ll Learn in AI GraphQL API Design: Schemas, Resolvers, and Federation
AI GraphQL API Design: Schemas, Resolvers, and Federation
Design a production GraphQL API the way the best teams do — schema-first, federated, performant, and AI-augmented from schema diff to resolver fuzzing.
What You Will Learn
By the end of this course you will have shipped real work — not just watched videos. Every lesson pairs a curated video tutorial with a hands-on exercise and a production-ready artifact you keep.
- Design a schema-first GraphQL API that survives three years of product change
- Write resolvers that avoid the N+1 trap with DataLoader and batched fetches
- Split a monolith into federated subgraphs with Apollo Federation 2
- Secure and throttle GraphQL APIs against depth/complexity attacks
- Use AI to diff schemas, generate fixtures, and fuzz resolvers before shipping
Full Course Breakdown
Lesson 1 (this page) is a free preview. Lessons 2 through 8 are the paid curriculum:
GraphQL in 2026: Where It Wins, Where It Doesn't
GraphQL hype peaked in 2019 and crashed in 2022. In 2026 it has stabilized into three clear use cases where it is strictly better than REST, and three where it's actively worse. We…
Schema-First Design: Modeling the Graph
The schema is the API contract — it outlives your codebase. Schema-first means you sketch types and relationships in SDL before you write a line of resolver code. This lesson walks…
Resolvers and the N+1 Problem
The most famous GraphQL performance trap: asking for 20 blog posts with their authors triggers 21 database queries. DataLoader fixes it in a dozen lines of code — if you wire it ri…
Federation: Splitting the Monolith
Apollo Federation 2 lets multiple services contribute to one supergraph. Done right, it's how big companies ship GraphQL at scale. Done wrong, it turns every schema change into a 4…
Security and Rate Limiting in GraphQL
A POST /graphql endpoint accepts literally any query shape. That surface is a security gift to attackers unless you add depth limits, complexity budgets, and persistent operations….
AI-Augmented GraphQL: Schema Diff, Fixtures, and Fuzzing
AI is a force-multiplier for GraphQL specifically because schemas are structured, well-typed, and extremely readable. In this lesson we use Claude/GPT to automate schema diff revie…
Observability and Continuous Delivery for GraphQL
A GraphQL request is a tree, so you can't just log 'endpoint latency'. You need per-field tracing, operation-level SLOs, and a CD pipeline that catches schema regressions before th…
Who This Course Is For
- Backend engineers evaluating REST → GraphQL migration
- Full-stack teams shipping their first production GraphQL endpoint
- Platform engineers owning a federated supergraph
- Engineering leads weighing the real cost of GraphQL in production
What Makes This Course Different
This is a hands-on, AI-native curriculum built for practitioners who want to ship, not sit through theory. Every concept is backed by a real-world exercise, a video walkthrough from a verified evergreen source, and a takeaway artifact. No filler, no fluff, no talking-head overload. Content is designed to stay relevant as the tooling evolves — we focus on principles and workflows that outlast any single tool release. Your progress is saved to your Vibe Coding People profile and the full community is one click away when you get stuck.
Ready to Get Started?
Unlock all 8 lessons, downloadable assets, and community access.