Evaluate an AI system
Learn how to distinguish system layers, inspect a claim, read a benchmark, and compare candidates with context intact.
Steps
7 steps-
Model, product, and platform are different layers
Identify which layer is actually being discussed.
A model generates predictions, a product organizes an experience, and a platform supplies infrastructure and controls for building systems. Comparing layers as peers creates category errors—for example, contrasting a chatbot subscription with an API model benchmark.
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How to read a model claim
Turn a capability statement into questions you can verify.
A model claim is useful only when it names the model version, task, conditions, metric, and source. System cards are stronger evidence than marketing adjectives, but independent replication and workload-specific tests answer different questions.
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Model evaluation
Separate the evaluation design from the resulting score.
Model evaluation measures defined behavior on specified tasks, data, prompts, and scoring rules. A benchmark score is evidence for that setup, not a universal ranking across cost, safety, latency, and real workloads.
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MMLU
Practice reading one benchmark definition and its limits.
MMLU evaluates broad text-only knowledge recall and problem solving across the sampled school, university, and professional domains.
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SWE-bench Verified — historical release
Compare a different task shape, setup, and unit of evidence.
SWE-bench Verified — historical release evaluates agent-plus-model ability to navigate Python repositories and produce test-passing patches for selected real-world issues.
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Open source, open weight, and open access
Check distribution and access terms independently of performance.
Open source refers to license-granted freedoms for source artifacts, while open weights describes parameter availability and open access describes reachability. A public paper, repository, or model page does not by itself grant permission to redistribute every artifact it references.
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Choosing an AI system
Synthesize the evidence into a decision frame.
Choosing an AI system requires workload evidence across quality, latency, cost, privacy, safety, and operational fit. A leaderboard can narrow candidates but cannot replace tests on representative prompts, tools, failure modes, and traffic patterns.