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, tests, and terminal output — the real working timeline, not just the end state.
- Autopilot signals — pasting AI output without reading or verifying it.
The six axes of AI fluency
AI-native assessments treat working with AI as a measurable skill. Taali scores it across six axes:
Prompt quality
Scoped, sequenced prompts vs. vague, cold ones.
Error recovery
Catching, rejecting, or verifying incorrect AI output.
Context utilization
Giving the model the right context to be useful.
Independence
Where they delegate to AI vs. own the reasoning themselves.
Design thinking
Whether they thought before they prompted.
Debugging strategy
How they isolate and fix problems with AI in the loop.
The result: a candidate standing report. Each axis 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 calibratedly rather than 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 terminal — and the runtime captures every prompt, paste, edit, and test. Those traces feed the six-axis rubric 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 six axes — prompt quality, error recovery, context utilization, independence, design thinking, and debugging strategy.