Beginner to Hero: Generative AI Development: full course guide
Build a generative AI product from an empty directory to production: project setup, a development framework for prompts and retrieval, testing when the output is not deterministic, an evaluation framework you can run in CI, and deployment with cost, tracing and rollback.
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What is inside this guide
01Getting Set Up
- What You Are Actually Building and Why
- Choosing a Model and a Provider Without Locking Yourself In
- A Project Skeleton That Survives Its First Six Months
- Keys, Secrets and the Local Development Loop
- Your First Call, End to End, With the Cost Printed
02The Development Framework
- Prompts as Versioned Artefacts, Not Strings in Code
- Structured Output and Why Parsing Free Text Fails
- Retrieval: Chunking, Embedding and the Index
- Context Assembly and the Token Budget
- Streaming, Timeouts and Partial Results
03The Testing Framework
- What a Test Means When the Output Is Not Deterministic
- Golden Sets, Snapshots and Tolerances
- Mocking the Model so the Suite Stays Fast and Free
- Property Tests for Structure, Safety and Refusals
- Regression Testing a Prompt Change Before It Ships
04The Evaluation Framework
- Choosing Metrics That Reflect the Job, Not the Demo
- Human Labels, Rubrics and Inter-Rater Agreement
- Model-Graded Evaluation and Its Failure Modes
- Retrieval Quality Measured Separately From Generation
- Running Evaluation in CI and Reading the Result
05The Deployment Framework
- Packaging, Configuration and Environment Parity
- Rate Limits, Retries, Backpressure and Circuit Breakers
- Caching, Batching and the Cost Model You Can Defend
- Observability: Traces, Logs and What You Must Not Capture
- Rollout, Rollback and the First Week in Production
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