Testing & Findings

Testing methods

How to find the limits of AI systems. Each method is backed by the real findings it has surfaced — and ranked by how much it surfaces, weighted by severity. The durable knowledge is the technique, not the patched-away bug.

Most productive methods

  1. 1Differential testing7 findings · 7 versions
  2. 2Prompt-injection & jailbreak testing4 findings · 4 versions
  3. 3Boundary & edge-case testing7 findings · 6 versions

Ranked by a severity-weighted yield score. Why we measure this →

Clear

2 methods

Boundary7 findings · 6 versions

Boundary & edge-case testing

Push inputs to limits — very long contexts, token boundaries, empty/extreme values — where behavior tends to degrade sharply.

3 Medium4 Low
EstablishedEvalsContext window
Boundary1 finding · 4 versions

Needle-in-a-haystack (long-context retrieval)

Plant a specific fact at varying depths in a long context and test whether the model can retrieve it from each position.

1 Medium
EstablishedRAGContext window