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To understand AI-generated code, you need a learning-first explanation tool that breaks down functionality, rationale, and usage context. This approach converts AI output into structured file-by-file guides that teach what the code does and why design decisions were made.

Explaining design decisions in AI code requires analyzing why specific patterns were chosen, including trade-offs and alternatives. A dedicated learning framework can automatically generate educational deep dives that detail the reasoning behind each implementation choice.

Generating markdown deep-dive documentation for recently written code involves invoking a post-task learning process. The system organizes explanations by component and implementation phase, producing structured markdown outputs that map the code's functionality and context.

Yes, a multi-language learning framework can generate explanations across different languages and frameworks. It uses the same educational structure to break down functionality and design rationale, ensuring consistent learning regardless of the coding language used.

AI code explanation works for both manual invocations and post-task learning scenarios. By using YAML-defined trigger phrases, the system captures recently written code and generates phase-aware, educational reports for users who want to review and understand their implementations.

Attaching curated learning resources to AI code explanations involves linking high-quality external references within the generated deep-dive reports. This deepens understanding beyond the immediate code by connecting the implementation to broader educational materials and concept mappings.