Anthropic's Free Prompt Engineering Course Hits 38,000 GitHub Stars
Anthropic's free, nine-chapter prompt engineering course on GitHub has crossed 38,000 stars, offering hands-on Jupyter notebooks with graded exercises against the Claude API.
- Anthropic's Interactive Prompt Engineering Tutorial has crossed 38,000 GitHub stars and 4,200 forks.
- Nine Jupyter chapters plus appendix cover beginner structure through advanced chaining, tool use, and retrieval.
- Every lesson has an Example Playground and graded exercises against expected Claude outputs.
- Free and open source; only cost is API usage, defaulting to cheap Claude 3 Haiku.
- Also available as a Google Sheets version for non-developers.
- Pair with Anthropic's context engineering essay for agent-era techniques not covered here.
Anthropic's prompt course passes 38,000 GitHub stars
Anthropic's prompt engineering tutorial has surpassed 38,000 GitHub stars and 4,200 forks. The course materials teach developers to write, run, and debug prompts against Claude from Jupyter notebooks. Its combination of first-party guidance, executable examples, and checked exercises gives developers a structured way to test prompt revisions.
Nine chapters, tighter constraints
The curriculum groups nine chapters and an appendix into progressively harder tiers:
| Tier | Chapters | Coverage |
|---|---|---|
| Beginner | 1–3 | Prompt structure, clear instructions, and role assignment. |
| Intermediate | 4–7 | Separating instructions from data, formatting responses, assistant prefills, structured reasoning, and few-shot examples. |
| Advanced | 8–9 | Reducing hallucinations and assembling complex prompts for chatbots, legal review, financial extraction, and coding tasks. |
| Appendix | Additional modules | Prompt chaining, tool use, and retrieval of external context. |
Each lesson includes an “Example Playground” where developers can edit prompts and rerun them against Claude. Exercises use grader functions or expected-output criteria to expose failures that a prose-only tutorial would leave hidden.
The repository also links to an answer key and a Google Sheets edition. The notebook version offers direct API experimentation, while the spreadsheet provides another way to follow the course without maintaining a local Jupyter environment.
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