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A participant case note showing why a strong AI-assisted synthesis still needs a separate, deliberately sceptical verification pass against its sources.

Put the framework to work
Two Prune uses realistic work simulations to produce structured assessment evidence—not automated employment decisions.
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3 August 2026 · 3 min read
Her assessment revealed a workflow shaped by early corporate AI training: use AI to write, then manually check everything.

28 July 2026 · 5 min read
Two Prune is beginning a research programme—and inviting academic collaboration—to develop task-bounded AI-fluency assessment into a responsible measurement method.
Synthesis asks: what does this material add up to? Verification asks: can every important part of that answer survive contact with the evidence? Combining both jobs in one instruction encourages the model to continue the same line of reasoning. Separating them creates a deliberate change of stance—from constructing the answer to trying to break it.
That distinction matters because a polished deliverable can coexist with a missed contradiction. Strong authorship can coexist with an incomplete verification process. A realistic work simulation creates an opportunity to observe both.
This is evidence from one participant completing one bounded assessment. It does not establish performance across every task or prove that the revised workflow will always prevent errors. For this participant, however, the report identified a practical change she considered useful enough to adopt.
That is the kind of development insight Two Prune is designed to surface: not simply whether someone uses AI, but where a small change in how they use it may strengthen the work. It also illustrates why an assessment needs a visible evidence chain from task to interpretation.
Practical prompt: “Review this deliverable against the supplied source material. For every material claim, identify its supporting evidence, conflicting evidence and any unsupported assumptions. Do not rewrite the deliverable yet.”
24 July 2026 · 3 min read
A stray AI editing note in a legislative speech shows why exploration, production and delivery need clear boundaries.