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quarto_book/tutorial-coding.qmd

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We draw inspiration from [Jeremy Howard ("Solve It With Code")](https://www.answer.ai/posts/2024-11-07-solveit.html) and [Simon Willison ("Using LLMs to write code")](https://simonwillison.net/2025/Mar/11/using-llms-for-code/).
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## What we do & why
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- **MCPs for context**: Use Model Context Protocol tools like [context7](https://context7.com/) to provide LLMs with up-to-date API documentation and project context.
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- When using **Claude Code** or agents, first use another LLM to write **detailed instruction / spec**: tests to write, constraints, error cases, style, behavior.
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## Essential Practices
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- **Use very detailed instructions when using an agent**: We often use one LLM to write the spec/instructions, then feed that into another or into an agent for implementation.
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- **Leverage MCPs**: Connect tools like context7 for real-time API docs, codebase context, and dependency information.
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## Workflow
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6. **Review & Improve**
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Use bots + human review to catch style, inefficiencies, potential pitfalls.
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## Tool-Specific Guidance
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Provide specific remediation steps."
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```
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## Security Considerations
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[Indirect injection attacks hide malicious instructions in web content that activate when LLMs summarize pages, bypassing traditional security measures because AI agents operate with broad authentication access.](https://brave.com/blog/comet-prompt-injection/)
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### Essential Security Practices
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- [ ] **Never trust AI-generated auth/payment/data-handling code** without expert review
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- [ ] **Validate all inputs** with comprehensive schemas and sanitization
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- [ ] **Enable GitHub secret scanning** and push protection for MCP workflows

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