AI Evaluation Frameworks

From NIST AI RMF to OpenAI Evals to SigRank — the landscape of AI evaluation frameworks spans governance, models, outputs, and operators. SigRank is the framework for the operator layer.

Every layer has a framework. Except the operator layer — until now.

NIST AI RMF governs. OpenAI Evals benchmarks models. DeepEval scores outputs. SigRank evaluates operators. The stack is now complete.

NIST AI RMF

A governance framework for managing AI risks. Defines four functions: Govern, Map, Measure, Manage.

Layer: governance
Scope: organizational AI risk
Type: voluntary, non-regulatory

OpenAI Evals

A framework for evaluating AI models on custom and standardized tasks. Open-source, extensible, model-agnostic.

Layer: model
Scope: model capability
Type: technical, open-source

DeepEval & Braintrust

Frameworks for evaluating AI outputs — unit-testing for LLM outputs and production evaluation pipelines.

Layer: output
Scope: output quality and correctness
Type: technical, CI/CD-integrated

SigRank

The framework for the operator layer. Public, content-free, governed by MO§ES. Measures the human using AI.

Layer: operator
Scope: human operator skill
Type: public, governed, continuous

SigRank as an evaluation framework

SigRank is more than a leaderboard — it is a framework for operator evaluation. It defines what to measure (Yield, computed from four token pillars: input, output, cache-read, cache-write), how to measure it (token telemetry from real sessions, never prompt content), and what the results mean (operator skill, ranked publicly with class tiers from NOVICE to SINGULARITY).

The framework is governed by MO§ES, which defines the measurement specification, privacy boundaries, and public accountability requirements. This makes SigRank suitable for compliance contexts where auditable, governed evaluation is required.

Υ = (cache_read × output) / input²

AI Operator Standard

The Upsilon measurement specification for the operator layer.

Public Operator Evals

The public evaluation layer for AI operators — ranked by Yield (Υ).

AI Operator Metrics

The metrics behind operator evaluation — Yield, Velocity, Leverage, and more.

AI Evaluation

The four layers of AI evaluation and where the operator layer fits.

FAQ

What are AI evaluation frameworks?: AI evaluation frameworks are structured methodologies for assessing AI system performance. They range from governance frameworks (NIST AI RMF, EU AI Act) to technical frameworks (OpenAI Evals, DeepEval, Braintrust) to operator frameworks (SigRank). Each framework defines what to measure, how to measure it, and what the results mean.
How does SigRank differ from other AI evaluation frameworks?: SigRank is the only framework for the operator layer. NIST AI RMF is a governance framework for AI risk management. OpenAI Evals is a framework for model evaluation. DeepEval and Braintrust are frameworks for output evaluation. SigRank evaluates the human operator using token telemetry — Yield (Υ) = (cache_read × output) / input² — and is public, content-free, and governed by the MO§ES framework.
What is the NIST AI RMF?: The NIST AI Risk Management Framework (AI RMF) is a voluntary governance framework for managing risks associated with AI systems. It defines four functions: Govern, Map, Measure, and Manage. The Measure function calls for continuous, auditable evaluation of AI systems. SigRank supports the Measure function by providing governed, continuous operator evaluation.
How do I choose an AI evaluation framework?: Choose based on what you need to evaluate. For governance and compliance, use NIST AI RMF. For model benchmarking, use OpenAI Evals or Vals AI. For output quality, use DeepEval or Braintrust. For operator evaluation, use SigRank. Most organizations need multiple frameworks — one per layer of the AI evaluation stack.

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SigRank as an evaluation framework

Υ = (cache_read × output) / input²

FAQ

What are AI evaluation frameworks?

AI evaluation frameworks are structured methodologies for assessing AI system performance. They range from governance frameworks (NIST AI RMF, EU AI Act) to technical frameworks (OpenAI Evals, DeepEval, Braintrust) to operator frameworks (SigRank). Each framework defines what to measure, how to measure it, and what the results mean.

How does SigRank differ from other AI evaluation frameworks?

SigRank is the only framework for the operator layer. NIST AI RMF is a governance framework for AI risk management. OpenAI Evals is a framework for model evaluation. DeepEval and Braintrust are frameworks for output evaluation. SigRank evaluates the human operator using token telemetry — Yield (Υ) = (cache_read × output) / input² — and is public, content-free, and governed by the MO§ES framework.

What is the NIST AI RMF?

The NIST AI Risk Management Framework (AI RMF) is a voluntary governance framework for managing risks associated with AI systems. It defines four functions: Govern, Map, Measure, and Manage. The Measure function calls for continuous, auditable evaluation of AI systems. SigRank supports the Measure function by providing governed, continuous operator evaluation.

How do I choose an AI evaluation framework?

Choose based on what you need to evaluate. For governance and compliance, use NIST AI RMF. For model benchmarking, use OpenAI Evals or Vals AI. For output quality, use DeepEval or Braintrust. For operator evaluation, use SigRank. Most organizations need multiple frameworks — one per layer of the AI evaluation stack.