Builds evaluation suites with ground-truth answers, automated scoring, and regression detection so you can measure whether model or prompt changes actually improve outcomes before shipping. Without systematic evaluation, teams ship changes that seem better anecdotally but may degrade specific edge cases silently.
Use cases
- Comparing two AI models (or two prompt variations) for a specific task and needing data to decide which to deploy
- Before shipping a prompt change to production and wanting to confirm it does not regress on known edge cases
- Running a weekly model evaluation to detect gradual quality degradation as the model version changes
- Evaluating fine-tuning results by measuring whether the fine-tuned model outperforms the base model on a held-out test set
- Benchmarking AI feature latency and cost alongside quality metrics to make deployment decisions
Key features
- Define task-specific metrics that reflect actual user value—not just generic perplexity or accuracy, but metrics tied to the specific behavior you care about
- Curate an evaluation dataset with diverse, representative inputs and ground-truth answers that reflect what good output looks like for your use case
- Run automated scoring against the evaluation dataset, comparing the new model or prompt against the baseline using statistical tests to determine if differences are significant
- Integrate evaluation runs into CI so that prompt or model changes that regress on eval metrics are blocked before merge
- Report eval results with confidence intervals, not just point estimates—small eval sets with single-run metrics are misleading
When to Use This Skill
- When making a model or prompt change that affects AI feature quality and you need objective data to validate the change
- When shipping AI features to a new domain and needing to confirm quality meets the bar before rollout
- When evaluating AI vendors or model versions and needing a repeatable benchmark to compare against
Expected Output
An evaluation suite with diverse test inputs, automated scoring, statistical significance testing, and a CI integration that blocks regressions.
Frequently Asked Questions
- How many evaluation examples do I need?
- At minimum 50-100 examples for statistical significance on simple tasks. For rare edge cases or high-stakes outputs, you may need hundreds. The key is diversity—100 examples that cover the input space well beats 1,000 repetitive examples.
- What metrics should I use beyond accuracy?
- Depends on the task: for code generation, use unit test pass rates; for summarization, use ROUGE or BERTScore against human references; for factual Q&A, use citation accuracy; for safety, measure false positive and false negative rates. Match the metric to the failure mode you care about.
- How do I detect when my evaluation set is overfitting to my model's strengths?
- Use a held-out test set that is not used during prompt or model development. If performance on the test set diverges significantly from your development eval set, your development set may be biased. Rotate evaluation sets periodically.
Related
Related
3 Indexed items
OpenAI GPT-5.6 and ChatGPT Work due diligence
Structures Yahoo Tech (Axios) reporting on July 9, 2026 that OpenAI broadly released GPT-5.6 and launched ChatGPT Work into a rollout, security, and enterprise-spend checklist. The workflow separates verified facts—OpenAI released three GPT-5.6 flavors (Sol strongest, Luna for speed, Terra balanced for everyday work); Sol includes an ultra mode that works harder and delegates to submodels; CEO Sam Altman told CNBC Sol is 54% more token efficient on agentic coding tasks; ChatGPT Work gathers context across connected apps and files to create documents, spreadsheets, and presentations and can work across web, phones, and computers, first on Mac and Windows apps for all tiers with web to follow; rollout was staggered after a U.S. government request to delay; Altman said many changes followed a collaborative back and forth and called government technical capabilities impressive; Yahoo Tech notes tester sentiment that Anthropic Fable may have greater raw intelligence while GPT-5.6 is viewed as more reliable for regular tasks—from internal model-access and spend planning. Distinct from anthropic-fable-mythos-export-ban-lifted-due-diligence tracking export-control lift.
Samsung ChatGPT Enterprise and Codex deployment due diligence
Structures AI News reporting on June 24, 2026 about Samsung Electronics expanding employee access to ChatGPT Enterprise and Codex into a security, procurement, and workforce-governance checklist. The workflow separates verified facts—OpenAI said deployment covers all Samsung Electronics employees in Korea and all Device eXperience employees worldwide; Samsung plans use across software development, marketing, product development, manufacturing, and other functions for search, drafting, idea development, data interpretation, and code work; rollout follows 2023 restrictions after sensitive internal information was uploaded to external AI; new access uses ChatGPT Enterprise with data protection, user access, and security controls; Codex supports code write/review/debug plus internal tools, websites, prototypes, and automated workflows; OpenAI said Codex has 5M+ weekly users and Korea Codex WAU grew nearly 800% since Feb 1, 2026; Harrison Kim (OpenAI Korea GM) called it one of OpenAI's largest enterprise deployments; October 2025 Samsung memory partnership for Stargate and Samsung SDS reseller/consulting links cited—from internal rollout decisions. AI News also cites Deloitte 66% productivity gains and 53% improved insights from enterprise AI adoption surveys.
Postmortem trigger and root-cause taxonomy
Distills Appendix C (“Results of Postmortem Analysis”) from Google’s SRE workbook: it explains why Google catalogs standardized postmortem fields—linking outages to observable triggers versus deeper root-cause categories—so reliability leaders can prioritize systemic fixes rather than anecdotal fixes. The appendix cites a multi-year corpus (labeled 2010–2017 in the workbook) highlighting that binary pushes accounted for roughly 37% of outage triggers while configuration pushes were about 31%, with additional slices for user-behavior spikes, pipelines, upstream providers, performance decay, capacity, and hardware. A companion table correlates outages with qualitative root causes such as faulty software (~41%), development-process gaps (~20%), emergent complexity (~17%), deployment planning weaknesses (~7%), and network failures (~3%). Teams use these distributions to sanity-check whether their incident queues skew differently and to steer investment into the failure classes that statistically dominate historically.