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A robot crosses uneven puzzle-topped cliffs where green task platforms succeed beside a crumbling pink task platform, as an evaluator holds separate check and cross markers.
Reviewed visual · version 1

RS Glossary

Jagged Capabilities

Jagged capabilities are the uneven pattern of AI performance in which a system can excel at one task yet fail at another that appears similar in subject or difficulty.

Review state
Reviewed in POC
Freshness
Current
Version
1
  • Jagged Capabilities
  • Jagged Capability Frontier
  • Jagged Technological Frontier

Remember it this wayCapability has cliffs, not a smooth edge.

In plain language

An AI capability boundary is not a smooth line from easy to hard. A model may perform expert-looking work on one assignment, miss a simpler-looking neighboring assignment, and give equally confident responses to both. The edge of what it can do has cliffs and pockets.

Why it matters

A strong demonstration does not establish broad competence. Teams must test the actual tasks, variations, and failure conditions they plan to use because intuitive judgments about which nearby tasks should also work are often unreliable.

Example

A model produces a strong market overview and persuasive interview plan, then gives a factually wrong recommendation on an adjacent analytical task that uses the same case materials. The polished earlier work did not prove capability across the whole workflow.

Common misconception

Jagged capabilities are not the same as random output or Model Drift. The uneven boundary can be repeatable at a given time. The lesson is that capability in one area does not justify assuming capability in a seemingly similar area.

Related terms

Sources

Learn more

  • Preserve task-specific evidence through [[capability-provenance|Capability Provenance]].
  • Use [[evaluation-gap|Evaluation Gap]] to identify where a successful test has been stretched into an unsupported capability claim.

Public history

30 recorded events · 30 public summaries
  1. A potential shared concept was discussed and selected for initial glossary drafting.

  2. The captured candidate received a complete initial draft and entered section-level human review.

  3. Radar reviewed the initial definition draft for human consideration.

  4. Radar reviewed the initial plain-language-explanation draft for human consideration.

  5. Radar reviewed the initial why-it-matters draft for human consideration.

  6. Radar reviewed the initial example draft for human consideration.

  7. Radar reviewed the initial common-misconception draft for human consideration.

  8. Radar reviewed the initial remember-it-this-way draft for human consideration.

  9. Radar reviewed the initial related-terms draft for human consideration.

  10. Radar reviewed the initial sources draft for human consideration.

  11. Radar reviewed the initial learn-more draft for human consideration.

  12. Definition review recorded as approve.

  13. Plain-Language Explanation review recorded as approve.

  14. Why It Matters review recorded as approve.

  15. Example review recorded as approve.

  16. Common Misconception review recorded as approve.

  17. Remember It This Way review recorded as approve.

  18. Related Terms review recorded as approve.

  19. Sources review recorded as approve.

  20. Learn More review recorded as approve.

  21. All wording and media review gates are approved; the term is now canonical.

  22. Definition review recorded as approve.

  23. Plain-Language Explanation review recorded as approve.

  24. Why It Matters review recorded as approve.

  25. Example review recorded as approve.

  26. Common Misconception review recorded as approve.

  27. Remember It This Way review recorded as approve.

  28. Related Terms review recorded as approve.

  29. Sources review recorded as approve.

  30. Learn More review recorded as approve.

Source: vault/Glossary/terms/jagged-capabilities/term.md · sha256:8d808446af89a61e8a501d5e4c0505ae575cf9522acc8abaf8fd1aabdadfc79b · Release: rs-glossary-glossary-team-poc-0.5.5-2026-09-02