ML engineering / DataAnnotation
Plausible is easy. Reliable takes a closer look.
Evaluating frontier models for code quality, reasoning, and agentic tool use.
01 →Model outputs
02 →Code & reasoning review
03 →Failure analysis
04 ✓Structured feedback
The challenge
A generated answer can look convincing while hiding a broken assumption or subtle implementation issue. Evaluating frontier models means looking past surface-level correctness to assess reasoning, instruction following, and reliability.
The engineering
- Evaluate model outputs for code generation, debugging, reasoning quality, and instruction following.
- Identify failure modes in multi-step reasoning, tool-use workflows, and agentic coding tasks.
- Provide comparative rankings and failure-mode analysis for reinforcement learning from human feedback (RLHF) pipelines.
The result
RLHF
Structured human feedback surfaces plausible-but-wrong code and reliability issues that automated benchmarks can miss.
05.Open a connection
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