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Robustness

As explained in The AI Pocket Book

Robustness is the ability of a system or model to maintain acceptable behavior or performance despite variations, disturbances, faults, or challenging conditions.

Robustness means delivering software that keeps working through edge cases and unfortunate situations, not merely producing code that works once. section 5.6

Build robustness by making validation part of development:

  • Think through what could go wrong with each small unit of code. section 5.6
  • Write individual tests for those cases, sometimes before writing the implementation. section 5.6
  • Keep useful tests in the codebase instead of replacing them with temporary print checks. section 5.6
  • Balance coding and testing rather than spending effort on implausible scenarios. section 5.6

A validation-driven team

Instead of implementing features first and testing later, a team intertwines development and testing, checking edge cases as each unit is built. section 5.6

How The AI Pocket Book covers it

How deep it goes
Explains robustness procedurally through edge-case analysis, unit tests, persistent checks, and testing tradeoffs.
Examples
Uses edge-case tests, print statements, small code units, and validation-driven team workflows as concrete examples.
What it assumes
Assumes software implementation experience and teaches how to supplement coding with systematic validation.
What you can do after
Apply it via test-driven development by designing tests for failure cases and keeping valuable checks in the codebase.
How it connects
Connects robustness with software testing, validation, test-driven development, unit testing, and edge cases.

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of The AI Pocket Book. The section links point to the parts of the book it draws on.