Guide
AI-native assessments
Stop testing whether a candidate can code without AI. Start measuring how well they code with it — the way the job actually works.
An AI-native assessment puts a candidate in a real, browser-based workspace with AI tools in hand and measures how well they use AI to do the work — instead of banning AI tools and testing whether they can code without them.
Why ban-AI assessments are broken
Most technical assessments still lock candidates out of AI: no Copilot, no Claude, no Cursor. But engineers use those tools every day, so a ban measures an artificial version of the job. Worse, it is increasingly unenforceable — candidates use AI anyway, off-screen. The honest move is to bring AI into the assessment and score how it is used.
What an AI-native assessment captures
Instead of grading only the final code, an AI-native assessment records the whole working session and ties every signal back to the report:
- Every prompt — was it scoped and sequenced, or vague and hopeful?
- Error recovery — did the candidate catch and correct a wrong AI suggestion before it shipped?
- Edits and sandboxed test runs — the real working timeline, not just the end state.
- Autopilot signals — pasting AI output without reading or verifying it.
The five dimensions of AI fluency
AI-native assessments treat working with AI as a measurable skill. Taali scores it across five dimensions — the 5 Ds of AI fluency:
Delegation
Knowing what to hand to AI and what to own — where they delegate vs. drive the reasoning themselves.
Description
How clearly they brief the model: scoped, sequenced, context-rich prompts vs. vague, cold ones.
Discernment
Judging AI output — catching, rejecting, or verifying a wrong suggestion before it ships.
Diligence
Thoroughness and verification: tests, edge cases, and responsible follow-through with AI in the loop.
Deliverable
The shipped result itself — does it actually work: code quality, tests, completeness, graceful failure.
Verification is scored, not assumed. Every task plants a trap the candidate should catch, and the assessment scores whether they verified their work before calling it done — Diligence rewards verified-before-done, not confident-and-wrong.
Every task is battle-tested before a candidate sees it. Each task is authored from the role's job description under a validation contract and human-approved, then run in a sandbox to confirm its baseline tests fail meaningfully — so the work sample is real, not a toy.
The result: a candidate standing report. Each dimension is scored from the captured session, and every score links back to the moment it happened in the prompt-by-prompt replay.
Keeping it fair
Allowing AI does not mean anything goes. A good AI-native assessment distinguishes genuine fluency from blind copying — flagging autopilot behaviour in a measured way, not punitively — and keeps scoring tied to evidence a human can review. Because the full session is captured, every score can be explained and audited.
How Taali's AI-native assessments work
On Taali, every assessment opens a chat-first workspace — Claude at the centre, your repo, a live editor, and a sandboxed runtime — and the runtime captures every prompt, paste, edit, and test. Those traces feed the five dimensions above, producing a candidate standing report with a prompt-by-prompt replay. It is the assessment half of AI-native hiring, and it feeds the recommendations made by Taali's agentic hiring pipeline.
Frequently asked questions
What is an AI-native assessment?
It puts a candidate in a real, AI-equipped workspace and measures how well they use AI to do the work — instead of banning AI and testing whether they can code without it.
If candidates can use AI, how do you stop cheating?
When AI is allowed, there is nothing to cheat with — using it well is the skill. The assessment captures the full session and flags autopilot behaviour like pasting without reading or verifying.
What does it measure?
Beyond whether the task works, it scores AI collaboration across five dimensions — delegation, description, discernment, diligence, and the deliverable itself. Verification is scored, not assumed: every task plants a trap the candidate should catch, and diligence rewards verifying before calling the work done. Every task is battle-tested in a sandbox before a candidate sees it — authored from the role's job description and human-approved.