AI Benchmarking - Guide | mysimulator.uk

AI Benchmarking – Guide

Metrics/datasets/protocols: how to correctly compare models/services and report.

Benchmarks & Acceptance (detailed)

Responsible AI Reporting – Guide”,

Prompt Engineering – Guide”, “Metrics: task-specific (F1/ROUGE/R@k), reliability (p95/p99), safety, cost.”] }],

heading”: “Datasets: Public/Internal; Layers of Complexity/Domains/Languages.”,

source_paragraphs”: [

Protocols: random seeds, power analysis, replications, transparent logging.

Reports: tables/graphs, conclusions/limitations, reproducibility kit.

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Frequently asked questions

What is AI benchmarking?

AI benchmarking involves systematically evaluating and comparing the performance of different AI models or services to determine their strengths, weaknesses, and suitability for specific tasks.

How do I avoid cherry-picking data when creating benchmarks?

To ensure a fair comparison, meticulously document your benchmark protocols and datasets, then publicly share all results including the exact methodology used.

What metrics should I use to evaluate generative AI models (GenAI)?

When assessing GenAI models, consider factors beyond just accuracy, such as factual correctness (‘groundedness’), tone of response, and always incorporate human review for a comprehensive evaluation.

How should I account for the costs associated with running AI benchmarks?

When comparing AI models, factor in all relevant costs including per-query cost, GPU usage, latency, and use these factors to define Service Level Objectives (SLOs) for performance monitoring.

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