The Five Eval Moves Fundamental Techniques for AI Evaluation

The Five Eval Moves Overview

Five fundamental moves form the foundation of practical evaluation: Run + Compare, Slice + Inspect, Simulate + Score, Integrate + Monitor, Escalate + Learn. These moves apply across domains and system types. Master these, and you can evaluate any AI system effectively.

Move 1: Run + Compare

Run your system on a benchmark. Run baseline systems on the same benchmark. Compare performance. This reveals how good your system is relative to alternatives.

Worked Example

Finance forecasting model. Run on Q4 data. Compare against: previous version (regression detection), competitor models (relative performance), human forecasters (skill baseline). Comparison reveals which version performs best, where improvement is needed.

Move 2: Slice + Inspect

Slice your evaluation data by cohorts (demographics, domains, difficulty levels). Inspect performance on each slice. This reveals disparate performance, hidden failure modes, dataset bias.

Worked Example

Credit decision model. Slice by: protected classes, credit amount, applicant age, geographies. Inspect performance on each slice. Discover model overprefers certain regions, underperforms on minority applicants. Slice analysis reveals biases that aggregate metrics hide.

Move 3: Simulate + Score

Create synthetic scenarios, stress tests, adversarial inputs. Simulate how your system handles edge cases, stress conditions, attacks. Score how well it handles them.

Worked Example

Chatbot safety. Simulate: user tries to manipulate bot into revealing secrets, user requests illegal activity, user probes for harmful information. Score how often bot falls for manipulation. Simulation reveals safety risks static evals miss.

Move 4: Integrate + Monitor

Deploy your system. Integrate monitoring. Continuously measure performance in production. Detect performance degradation, distribution shift, emergent failures.

Worked Example

Recommendation engine. Integrate monitoring. Track: click-through rate, user satisfaction, diversity. Monitor continuously. Detect drop in CTR. Investigate: dataset shift? Model degradation? Integrate + Monitor catches production issues.

Move 5: Escalate + Learn

When evals surface problems, escalate to decision-makers. Capture learnings. Improve eval process based on what you learn. This closes the loop from evaluation to improvement.

Worked Example

Fraud detection model. Eval detects 12% of fraud being missed. Escalate to decision-makers. Improve model. Capture learning: certain fraud patterns were underrepresented in training. Update eval to specifically catch these patterns. Learn and improve.

Move Zero: The Prerequisite

All five moves require this prerequisite: access to model outputs and ability to inspect them. If you cannot see what your model outputs, you cannot evaluate it. This seems obvious but is often violated in production systems with black-box vendor models.

Common Implementation Failures

Run + Compare: incomplete baselines, cherry-picked datasets. Slice + Inspect: slices too coarse-grained to reveal problems. Simulate + Score: simulations unrealistic. Integrate + Monitor: metrics poorly chosen, monitoring ignored. Escalate + Learn: problems flagged but not fixed.

Adapting Moves by System Type

Classifier evals: Run + Compare measures accuracy. Slice + Inspect finds performance disparities by class. RAG evals: Run + Compare on retrieval and generation separately. Slice + Inspect on query types. Agent evals: Simulate + Score on complex task sequences. Integrate + Monitor on action success.

The Moves in Sequence

Full eval sprint: (1) Run + Compare baseline. (2) Slice + Inspect for disparities. (3) Simulate + Score edge cases. (4) Integrate + Monitor in staging. (5) Escalate + Learn from production data. Repeat monthly or quarterly.

Measuring Move Effectiveness

Did the move work? Measure: problems found (did the move surface real issues?), decision impact (did findings change decisions?), time to fix (how fast could you act on findings?).

Teaching the Moves to Your Team

Run a workshop: 2 hours introduction, 4 hours hands-on case studies, 2 hours team practice. After workshop, team can apply moves independently. Template: teach move, show example, team practices, debrief.

Advanced Implementation and Mastery of the Moves

Move Combinations and Sequencing

The moves combine and sequence. A full eval program might: (1) Run + Compare baseline, (2) Slice + Inspect for issues, (3) Simulate + Score to test edge cases, (4) Integrate + Monitor in production, (5) Escalate + Learn to improve next version. The sequence is logical and builds information progressively.

Move Velocity and Iteration Speed

How fast can you execute the five moves? Ideally: move 1-2 in hours, move 3 in a day, move 4 immediately, move 5 continuously. Fast iteration speed enables rapid learning. Invest in automation and infrastructure to maximize move velocity.

The Cost of Each Move

Each move has costs: Run + Compare requires benchmark data and compute. Slice + Inspect requires skilled analysis. Simulate + Score requires scenario design. Integrate + Monitor requires infrastructure. Escalate + Learn requires organizational structures. Understand and budget for these costs.

When Not to Use Each Move

Not every situation requires all moves. Low-risk systems might skip Simulate + Score. Batch systems might skip Integrate + Monitor. Understanding when to apply which move is part of mastery. Evaluate: what decision does this move inform? If no decision depends on it, don't do it.

Domain-Specific Move Variations

Moves adapt to domain. For classification, Run + Compare emphasizes accuracy metrics. For RAG systems, it emphasizes retrieval and generation quality separately. For agents, it emphasizes action success. For safety-critical systems, Slice + Inspect and Simulate + Score are emphasized heavily.

Automating the Moves

Automate what you can. Automated Run + Compare executes benchmarks on new versions automatically. Automated Slice + Inspect runs on schedule identifying disparities. Automated Integrate + Monitor runs continuously. Automation multiplies what teams can accomplish.

Teaching Others the Five Moves

Transferring knowledge of the moves to your team accelerates capability building. Run workshops, pair program, create documentation. After team learns the moves, they can apply them independently. This is force-multiplication for your eval capability.

Variations and Extensions of the Moves

The five moves are foundational but can be extended. Examples: (6) Reweight + Rebalance (adjusting training data based on evaluation insights), (7) Cascade + Decompose (evaluating pipeline stages separately), (8) Ensemble + Combine (combining evaluations from multiple models). Advanced practitioners go beyond the core five.

The Moving Beyond Moves: Wisdom and Judgment

Mastery of the moves is foundational. But true mastery involves judgment: when to use which move, how deeply to apply it, when to skip it, when to combine moves in novel ways. This judgment comes from experience and mentoring from senior evaluators.

Common Mistakes When Implementing Moves

Common mistakes include: (1) Run + Compare with poor baselines, (2) Slice + Inspect too coarsely, (3) Simulate + Score with unrealistic scenarios, (4) Integrate + Monitor with poorly chosen metrics, (5) Escalate + Learn without acting on findings. Learning from these mistakes accelerates mastery.

The Moves in Crisis and High-Stress Situations

When production systems are failing, the moves provide structure. Move quickly through 1-2 to identify the problem. Move 3-4 to understand scope. Move 5 to communicate and fix. The moves provide methodology when panic is tempting.

Continuous Improvement of Your Move Practice

Keep a journal of your evaluations: which move identified which insights? What worked? What didn't? Review quarterly. Over time, you develop patterns about which moves are most effective for your systems. This continuous improvement accelerates mastery.

The Moves in Different Organizational Contexts

The Moves in Startups vs. Enterprises

Startups: move quickly through all five moves, probably spending days on the full cycle. Enterprises: each move takes longer but are more systematic. Early-stage startups might focus on moves 1-2 (understand what you're building). Mature organizations do all five systematically.

The Moves for Different Data Modalities

The moves adapt to data modality. Text eval: Run + Compare on text metrics. Image eval: Slice + Inspect on visual features. Multimodal eval: Simulate + Score across modalities. Audio eval: Integrate + Monitor on audio quality. Core principles remain constant; implementation varies.

The Moves for Streaming vs. Batch Systems

Batch systems: Run + Compare on historical data, Slice + Inspect on cohorts, Simulate + Score on synthetic scenarios. Streaming systems: Integrate + Monitor continuously, Escalate + Learn in real-time. The moves sequence differently based on system architecture.

Teaching the Moves to Non-Technical Teams

Business users can understand the moves. Run + Compare: how does our system compare to competitors? Slice + Inspect: are there groups where performance varies? Simulate + Score: what if this scenario occurred? Make the moves accessible to non-technical audiences.

The Moves in Regulatory and Compliance Contexts

In regulated environments, all five moves become mandatory. Documentation of each move is required. Escalate + Learn might involve regulatory reporting. The rigor required is higher. But the framework still applies.

Advanced Practitioners Extending the Moves

Mastery means extending the framework. Novel moves: (6) Interpolate + Extrapolate (understanding model behavior in regions outside training data), (7) Counterfactual + Explain (explaining why the model made a specific decision), (8) Federate + Aggregate (combining evaluations across multiple models into portfolio insights). These extensions emerge from deep practice.

Integration: Using All Five Moves Together

The Complete Evaluation Cycle

Full cycle using all five moves: (1) Run + Compare on baseline to establish starting point. (2) Slice + Inspect to understand where you stand across segments. (3) Simulate + Score to probe edge cases and stress conditions. (4) Integrate + Monitor in staging environment to validate before production. (5) Escalate + Learn from production data to drive next iteration. This cycle takes 4-6 weeks, runs monthly or quarterly, drives continuous improvement.

Move Velocity and Cycle Time

The Five Moves can be executed at different velocities. Slow cycle: each move takes days, full cycle takes weeks. Good for rigorous evaluation. Fast cycle: each move takes hours, full cycle takes days. Good for rapid iteration. Choose velocity based on risk: higher-risk systems demand slower, more rigorous cycles.

The Moves Under Resource Constraints

If you have limited resources: Run + Compare (essential, automate where possible), Slice + Inspect (essential, manually focus on high-risk slices), Simulate + Score (lower priority, focus on most dangerous scenarios), Integrate + Monitor (essential for critical systems), Escalate + Learn (essential, feedback drives improvement). Prioritize ruthlessly.

Scaling the Moves Across Teams

To scale moves across teams: (1) Train team on framework, (2) create templates for each move, (3) establish tools and infrastructure, (4) run shared platform where teams track moves, (5) share learnings across teams, (6) celebrate teams that use moves effectively. Scaling requires systematic approach.

The Moves for Different Stakeholders

Different stakeholders care about different moves. Engineers care about Run + Compare and Integrate + Monitor (practical insights). Product managers care about Run + Compare and Slice + Inspect (decision support). Leadership cares about Escalate + Learn (risk management). Design communication for each audience.

Continuous Improvement of Move Practice

As you execute moves repeatedly, improve: faster execution, deeper insights, better communication. Each cycle, ask: what went well? What could improve? Implement improvements. After 50-100 cycles of executing the moves, you develop mastery and intuition.

Beyond the Moves: Advanced Techniques

Once you master the five moves, explore advanced techniques: Causal analysis (not just correlation), Counterfactual analysis (what would have happened with different data?), Federated evaluation (across distributed systems), Meta-evaluation (evaluating your evals), Formal verification (proving properties). Advanced techniques build on foundation of five moves.

Final Thoughts: Evaluation as Craft

Evaluation is a craft. The five moves are tools. Like any craft, mastery comes from practice, study, mentorship, and reflection. Invest in developing evaluation expertise. It's increasingly valuable, increasingly important, and increasingly in demand. You can be excellent at this craft. Start with the moves. Build from there.

Master Evaluators: Study and Learn From

Learn From Practitioners

Study how practitioners at leading organizations apply the five moves. Google, Amazon, Meta, Microsoft, Anthropic, OpenAI have sophisticated evaluation practice. Many publish insights. Learn from their approaches, adapt to your context. Standing on giants' shoulders accelerates your learning.

Join Evaluation Communities

Join communities: AI evaluation forums, Slack groups, reading groups, conferences. Connect with other practitioners. Shared learning accelerates mastery. Community also provides networking, opportunities, collaborations. Don't learn in isolation.

Mentor and Be Mentored

Find mentor who has mastery in evaluation. Seek mentorship. As you develop expertise, mentor others. Mentoring cements your own knowledge and multiplies your impact. The mentorship cycle is how expertise transfers and communities strengthen.

Publish and Share Learnings

As you develop insights, publish: blog posts, papers, talks, podcast appearances. Sharing builds your authority and helps community. Your insights might save someone months of learning. Contribute to field. The field becomes stronger when practitioners share.

The Five Moves as Lifelong Practice

Continuous Practice and Improvement

Mastery of the five moves is not destination but lifelong practice. Each evaluation cycle teaches you something. Each mistake teaches you something. Each success teaches you something. Approach evaluation with learning mindset. Continuous practice yields mastery.

From Moves to Mastery

Mastery comes after consistent practice over years. Don't expect mastery in months. Expect steady progression: basic competence (months), intermediate skill (1-2 years), advanced skill (3-5 years), mastery (5+ years). Be patient with yourself. Deep mastery is rare and valuable.

The Joy of Evaluation Work

Beyond career and financial rewards, there's joy in evaluation work. The satisfaction of preventing a problem, the intellectual challenge of designing good evals, the impact of better decisions informed by good evaluation, the community of practitioners. This intrinsic joy sustains careers through ups and downs.

Final Word: Evaluation Matters

AI is increasingly important in society. Decisions based on AI have consequences. Evaluation ensures those decisions are good ones. Your work as an evaluator matters. The AI systems you help evaluate affect millions of lives. This importance, this impact, is why evaluation work is meaningful. Do it well.

Conclusion and Next Steps

Integration With Your Current Practice

This comprehensive guide covers deep expertise in this domain. The insights, frameworks, and best practices described here have been tested across hundreds of organizations and thousands of practitioner applications. As you read and study this material, consider: How do I apply this to my current role? What quick wins can I achieve? What long-term investments should I make? The gap between knowledge and application is where real learning happens. Close that gap through deliberate practice and reflection.

Building Your Personal Evaluation Philosophy

As you develop expertise, you'll synthesize your own evaluation philosophy. Your philosophy will reflect your values, your experiences, your organizational context, and your vision of what good evaluation looks like. This personal philosophy becomes your north star, guiding decisions and priorities. Developing this philosophy is part of the mastery journey. Write it down. Share it. Refine it over time as you learn more.

Contributing Back to the Community

As you gain expertise, contribute back. Write about your learnings. Speak at conferences. Mentor junior evaluators. Open source your tools. Contribute to standards. The evaluation community is young and rapidly developing. Practitioners like you shape its future through your contributions. The field needs your voice.

The Longer View: AI, Society, and Evaluation

Evaluation work matters beyond business outcomes. As AI becomes more powerful and more consequential, the quality of evaluation determines how well we deploy AI safely and beneficially. Your work as an evaluator contributes to this societal outcome. Take this responsibility seriously. Do excellent work. It matters.

Staying Current in a Rapidly Evolving Field

The evaluation field is evolving rapidly. New techniques emerge constantly. Regulatory landscape shifts. Best practices evolve. This requires commitment to continuous learning. Read papers, attend conferences, engage with community, experiment with new techniques. Make learning a permanent part of your practice. Professionals who stay current thrive; those who rely on dated knowledge struggle.

Building a Career in Evaluation

Evaluation is increasingly important field. Career prospects are strong. Multiple paths exist: practitioner, manager, officer, consultant, advisor, investor, researcher. Multiple sectors are hiring: tech, finance, healthcare, government, defense. Multiple geographies offer opportunities. If you're interested in this field, now is the time to develop expertise. The field is growing; opportunities are expanding.

The Mastery Mindset

Approach evaluation with mastery mindset. Mastery is a journey, not a destination. You'll never know everything. The field will always have aspects you're learning. This is not frustrating; it's exciting. It means growth is always possible. It means expertise is always deepening. Embrace this learning journey. Find joy in continuous improvement. This mindset sustains careers through decades.

Your Next Steps

Having read this comprehensive guide, what are your next steps? Consider: (1) Identify your biggest evaluation challenge in your current work. (2) Apply relevant frameworks and techniques from this guide. (3) Measure the impact. (4) Share learnings with your team. (5) Iterate and improve. (6) Build expertise through deliberate practice. This practical application transforms knowledge into skill. Do the work. Build the expertise. Create the impact.

Final Encouragement

Evaluation is challenging, important, and increasingly recognized as critical. The professionals who excel at evaluation are increasingly valuable. You have the opportunity to become excellent at this craft. The knowledge is here. The frameworks are here. The community is here. All that remains is commitment and practice. Commit to excellence in evaluation. The field, the companies you work with, and the society that depends on good AI decisions will be better for it.

Contact and Community

You're not alone in this journey. Thousands of evaluation practitioners worldwide are working on similar problems. Join eval.qa community, engage with other practitioners, contribute your voice. The evaluation community is welcoming and collaborative. Find your tribe. Learn together. Grow together. The best expertise comes through community, not isolation.

Thank You and Best Wishes

Thank you for engaging with this deep material on AI evaluation. Your commitment to learning and developing expertise is commendable. The field needs thoughtful, dedicated practitioners. Become one of them. Excel at evaluation. Build systems and organizations that deploy AI excellently. Create impact that matters. You have the knowledge, the frameworks, and now the comprehensive guide. Do the work. Build the expertise. Change the field for the better.

The Moves in Crisis and Emergency Situations

Emergency Evaluation When Incidents Occur

When production systems fail, evaluation is needed urgently. Compress the five moves: (1) Run + Compare to understand performance drop (take 1-2 hours), (2) Slice + Inspect to identify root cause (2-4 hours), (3) Simulate + Score to understand scope (2-4 hours), (4) Integrate monitoring rapidly, (5) Learn and improve. You can execute full cycle in 12-24 hours during crisis.

Evaluation Under Extreme Time Pressure

Normal evaluation takes days/weeks. Emergency evaluation compresses to hours. This requires: pre-existing benchmark infrastructure (no time to create from scratch), automated evaluation where possible, experienced team (no time for learning), clear priorities (focus on what matters most). Build capacity for rapid evaluation during normal times so you can execute in crisis.

The Moves in Safety-Critical Decisions

For safety-critical decisions, you need depth even if timeline is short. The five moves should be executed rigorously: Run + Compare uses largest possible baseline set, Slice + Inspect focuses on safety-critical segments, Simulate + Score emphasizes dangerous scenarios, Integrate + Monitor is continuous, Escalate + Learn escalates to highest levels. Safety doesn't compress.

Advanced Implementation Case Studies and Deep Dives

Real-World Implementation Challenge Case Study

Consider a real-world scenario: A company is deploying evaluation framework described in this guide. Initial obstacles: legacy systems hard to integrate, team resistance to new processes, limited budget for new tools, unclear ROI on upfront investment. How to overcome? Phased rollout: start with highest-impact system, demonstrate value, expand gradually. Buy-in from influencers on the team. Early wins build momentum. This is how organizational change happens: step by step, with small wins building to large transformations.

Overcoming Common Implementation Obstacles

Organizations implementing framework from this guide typically face common obstacles. (1) Technical integration: existing systems weren't built with evaluation in mind. Solution: adapters and integration layers. (2) Cultural resistance: evaluators see new process as bureaucratic. Solution: demonstrate efficiency gains and quality improvements. (3) Resource constraints: can't afford full implementation. Solution: phased approach, automation investments. (4) Metrics confusion: unclear which metrics matter. Solution: start with simple metrics, expand gradually. Every organization will face these obstacles. Anticipate them. Plan for them. Have mitigation strategies ready.

Benchmarking Implementation Challenges

Implementing benchmarking at scale faces unique challenges. Dataset quality: sufficient representative test cases? Tool infrastructure: can you execute benchmarks reliably? Reproducibility: can you reproduce results? Statistical rigor: do you have sufficient samples? Stakeholder alignment: do stakeholders agree on success criteria? Each challenge requires specific solutions. Address each systematically.

The Role of Tools and Infrastructure

Frameworks are conceptual. Tools are practical. Good evaluation requires infrastructure: experiment tracking, result storage, visualization, comparison tools, alert systems. Many organizations underinvest in tools. Paradoxically, tools save time and money by enabling scale and automation. Invest in tools early. They pay for themselves through productivity gains.

Building Evaluation SOPs

Success requires Standard Operating Procedures (SOPs). SOPs document: how to request evaluation, what information is needed, how evaluation is executed, timeline expectations, how results are communicated, how issues are escalated. SOPs enable consistency and scalability. They also enable delegation (new team members can follow SOPs). Invest in clear documentation.

Metrics Selection and KPI Definition

What are your Key Performance Indicators for evaluation program? Examples: percentage of systems evaluated, incident rate from systems with evals vs. without, time-to-evaluation, stakeholder satisfaction, budget efficiency. Clear KPIs focus effort and enable accountability. Define KPIs explicitly. Track them quarterly. Adjust strategy based on KPI trends.

Governance and Decision Rights

Who decides: which systems get evaluated, how resources are allocated, when evaluation findings override business pressure? Unclear decision rights lead to conflict. Establish explicit governance: evaluation committee structure, decision-making authority, escalation paths. Document and communicate. This prevents conflict and enables efficient decision-making.

Continuous Improvement and Iteration

Evaluation practice should improve continuously. Quarterly retros: what worked well? What didn't? What should we change? Implement changes. Measure impact. Iterate. This continuous improvement mindset transforms evaluation from static process to living practice that improves over time.

Scaling to Enterprise Size

Frameworks that work for startup (single team, 5 AI systems) don't automatically work for enterprise (multiple teams, 100+ AI systems). Scaling requires: standardization (consistent methodology across teams), delegation (central team can't evaluate everything), automation (tools do routine work), governance (clear decision-making structures), culture (evaluation is valued everywhere). Scaling is hard. Plan for it explicitly.

Lessons Learned from Field

Organizations implementing these frameworks report consistent lessons. (1) Start simple and expand: don't try to build perfect system from day one. (2) Focus on decisions: evaluation that doesn't inform decisions is waste. (3) Build gradually: cultural change takes time; don't force it. (4) Celebrate wins: share stories of evaluation success; use them to build momentum. (5) Invest in people: good evaluation requires skilled people; invest in hiring and development. (6) Invest in tools: tools enable scaling; they're not optional.

Measuring Success and Business Impact

How do you know if evaluation is working? Success metrics: (1) Incidents prevented (comparing systems with evals to those without), (2) Decision quality improvement (decisions informed by evals have better outcomes), (3) Deployment acceleration (evals enable faster confident deployment), (4) Team capability increase (team improves in evaluation skill), (5) Culture shift (evaluation becomes normal part of work). Track these metrics quarterly. Adjust strategy based on results.

The Path Forward

You've read this comprehensive guide covering deep domain expertise. The frameworks, methodologies, and best practices described here are battle-tested across real organizations. The next step is application. Choose one area where you can apply these ideas. Start small. Execute well. Measure impact. Expand. Build expertise through deliberate practice. Years from now, you'll have internalized these frameworks. They'll be part of your intuition. That's when you've truly mastered the domain. Get started. The journey is rewarding.