Unverified50% confidenceOpinionExact time
Dhinakaran argues that AI engineers should spend more time writing evals and need to "evaluate the evaluator": build golden-standard datasets, validate eval results against human outputs, and only release a new evaluator after ensuring the judge aligns with human judgment.
1
Sources
50%
Confidence
Long-term
Relevance
10/25/2026
First Seen
Sources
The AI Engineer's Role Has Shifted: From Writing Code to Building Agent Factories
bilibiliAIToBox10/25/2026
Related Entities
Related Claims
UnverifiedDhinakaran believes the core capability of AI engineers is newly defined as "defining what good looks like"; as agents take on more and more execution work, the value of human engineers concentrates on establishing and calibrating judgment criteria.72% similarVerifiedEvaluating AI should be done on a task-specific basis, focusing on reliability and how much human verification is required, rather than applying blanket optimistic or pessimistic judgments.70% similarVerifiedThe role of testers is shifting from executors to AI reviewers, and the prerequisite for harnessing AI is possessing foundational skills such as automated testing and performance testing.70% similarVerifiedWhen delegating complex tasks to AI, a sound code review and testing mechanism should be established to ensure that efficiency gains do not come at the cost of quality.70% similarUnverifiedThe standard for judging whether an AI use case is efficient is to look at the number of manual interventions: workflows that still require repeated copying and pasting, repeated context explanation, or manual triggering each time most likely have room for optimization69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1097959API
curl https://kongchang.com/api/v1/knowledge/claims/1097959MCP
get_claim(id=1097959)