DevOps AI Toolkit

# PRD 156: AI Evaluation Standards Compliance Framework ## Overview **Problem Statement**: The dot-ai toolkit has a powerful custom AI evaluation framework, but uses proprietary formats that don't integrate with industry-standard ML platforms (OpenAI Evals, LangSmith, MLflow), limiting research collaboration, industry credibility, and team productivity. **Solution**: Implement standards-compliant experiment tracking system that maintains all existing evaluation capabilities while adding compatibility with OpenAI Evals, LangSmith, MLflow, and statistical significance testing. **Success Metrics**: - 100% compatibility with OpenAI Evals framework - Export/import functionality with LangSmith and MLflow - Statistical significance testing for all model comparisons - Reproducible experiments across different environments ## Strategic Context ### Business Impact - **Industry Leadership**: First DevOps AI toolkit with full standards compliance - **Research Collaboration**: Enable academic partnerships and benchmark participation - **Team Productivity**: Integrate with existing ML platform workflows - **Credibility**: Follow established evaluation practices recognized by ML community ### User Impact - **Platform Integration**: Use familiar ML tools (MLflow UI, W&B dashboards) with dot-ai data - **Statistical Confidence**: Know when model improvements are statistically significant - **Reproducibility**: Reproduce evaluation results across different environments - **Benchmarking**: Compare dot-ai against other AI systems using standard metrics ## Current State Analysis ### Existing Evaluation Infrastructure (Strong Foundation) - **Comparative Evaluation Framework**: AI-as-judge methodology with weighted criteria - **Multi-Provider Support**: Claude, GPT-5, GPT-5 Pro dataset generation - **Tool Coverage**: Remediation, Recommendation, Capabilities, Patterns, Policies - **Dataset Generation**: 150+ evaluation datasets from real integration test runs - **Performance Analysis**: Speed, quality, token efficiency comparative analysis ### Current Limitations (Standards Gap) - **Proprietary Schema**: Custom JSONL format not compatible with industry tools - **No Statistical Testing**: Comparative scores lack significance testing - **Limited Export**: Can't export to MLflow, LangSmith, or W&B - **Non-Reproducible**: Missing experiment versioning and environment capture ## Technical Architecture (Standards-First Design) ### 1. Standard Experiment Schema ```typescript interface StandardExperiment { experiment_id: string; // UUID for experiment tracking name: string; // Human-readable experiment name description: string; // Experiment purpose and context dataset_name: string; // Reference to evaluation dataset models: string[]; // List of evaluated models evaluators: string[]; // List of evaluation criteria used // Core results following MLflow/LangSmith pattern results: { summary: { total_samples: number; duration_seconds: number; cost_usd: number; statistical_analysis: { p_value: number; // Statistical significance confidence_interval: [number, number]; // 95% confidence interval effect_size: number; // Cohen's d or similar significant: boolean; // p < 0.05 }; }; model_performance: Record<string, { avg_score: number; std_deviation: number; sample_count: number; category_scores: Record<string, number>; // Quality, Efficiency, etc. metadata: { avg_tokens: number; avg_response_time_ms: number; failure_rate: number; }; }>; }; // Standard metadata for reproducibility metadata: { created_at: string; // ISO timestamp framework_version: string; // dot-ai version environment: { platform: string; node_version: string; git_commit: string; }; tags: string[]; // Searchable tags parameters: Record<string, any>; // Evaluation parameters }; } ``` ### 2. OpenAI Evals Compatibility ```yaml # eval-configs/dot-ai-kubernetes-deployment.yaml (OpenAI Evals format) dot-ai-kubernetes-deployment: id: dot-ai-kubernetes-deployment.dev.v0 description: Evaluate Kubernetes deployment recommendations disclaimer: This evaluation tests dot-ai's deployment recommendation accuracy metrics: - accuracy - f1_score eval_spec: cls: evals.elsuite.basic.match:Match args: samples_jsonl: kubernetes-deployment-standard.jsonl match_fn: dot_ai_kubernetes_match completion_fns: - anthropic/claude-sonnet - openai/gpt-4 - openai/gpt-3.5-turbo ``` ### 3. Statistical Analysis Engine ```typescript interface StatisticalAnalysis { // Significance testing using appropriate statistical tests performSignificanceTest( modelA: EvaluationResults, modelB: EvaluationResults ): Promise<{ p_value: number; confidence_interval: [number, number]; effect_size: number; test_type: 'welch_t_test' | 'mann_whitney_u' | 'bootstrap'; significant: boolean; interpretation: string; }>; // Multi-model ANOVA for more than 2 models performMultiModelAnalysis( models: Record<string, EvaluationResults> ): Promise<MultiModelAnalysis>; // Power analysis for sample size recommendations calculateRequiredSampleSize( effect_size: number, power: number, alpha: number ): number; } ``` ### 4. Platform Export Adapters ```typescript // MLflow format export class MLflowExporter { async exportExperiment(experiment: StandardExperiment): Promise<MLflowRun> { return { run_id: experiment.experiment_id, experiment_id: this.getOrCreateMLflowExperiment(experiment.name), status: 'FINISHED', start_time: new Date(experiment.metadata.created_at).getTime(), end_time: new Date(experiment.metadata.created_at).getTime() + experiment.results.summary.duration_seconds * 1000, metrics: this.convertToMLflowMetrics(experiment.results), params: experiment.metadata.parameters, tags: Object.fromEntries(experiment.metadata.tags.map(tag => [tag, 'true'])) }; } } // LangSmith format export class LangSmithExporter { async exportExperiment(experiment: StandardExperiment): Promise<LangSmithDataset> { return { id: experiment.experiment_id, name: experiment.dataset_name, description: experiment.description, created_at: experiment.metadata.created_at, examples: await this.convertToLangSmithExamples(experiment), runs: await this.convertToLangSmithRuns(experiment) }; } } ``` ## Implementation Plan ### Milestone 1: Core Standards Infrastructure ⬜ **Target**: Implement standard experiment schema and data transformation **Key Deliverables:** - [ ] **StandardExperiment Interface**: TypeScript interface following MLflow/LangSmith patterns - [ ] **Data Transformation Pipeline**: Convert existing custom JSONL to standard schema - [ ] **Experiment Manager**: Core experiment tracking and storage functionality - [ ] **Backward Compatibility**: Maintain existing evaluation workflow while adding standards **Breaking Changes:** - [ ] **Add**: Standard experiment metadata to all evaluation runs - [ ] **Enhance**: Evaluation results with statistical metadata preparation - [ ] **Create**: Migration utility for existing evaluation data **Success Criteria:** - [ ] All new evaluations generate standard-compliant experiment records - [ ] Existing evaluation data can be migrated to standard format - [ ] No disruption to current evaluation workflow ### Milestone 2: Statistical Significance Testing ⬜ **Target**: Add rigorous statistical analysis to all model comparisons **Key Deliverables:** - [ ] **Statistical Analysis Engine**: Significance testing, confidence intervals, effect sizes - [ ] **Multi-Model ANOVA**: Statistical comparison of 3+ models simultaneously - [ ] **Power Analysis**: Sample size recommendations for reliable results - [ ] **Uncertainty Quantification**: Confidence intervals for all performance metrics **Breaking Changes:** - [ ] **Replace**: Simple comparative scores with statistically validated comparisons - [ ] **Add**: P-values and confidence intervals to all evaluation reports - [ ] **Enhance**: Model selection recommendations with statistical backing **Success Criteria:** - [ ] All model comparisons include p-values and confidence intervals - [ ] Statistical significance determines model ranking recommendations - [ ] Power analysis prevents conclusions from insufficient data ### Milestone 3: OpenAI Evals Compatibility ⬜ **Target**: Full compatibility with OpenAI Evals framework **Key Deliverables:** - [ ] **Standard Dataset Format**: Convert dot-ai datasets to OpenAI Evals JSONL format - [ ] **Evaluation Configs**: YAML configs for each dot-ai evaluation scenario - [ ] **Custom Match Functions**: Domain-specific matching for Kubernetes evaluations - [ ] **Integration Testing**: Verify evaluations run in actual OpenAI Evals framework **Breaking Changes:** - [ ] **Restructure**: Dataset storage to support both custom and standard formats - [ ] **Add**: OpenAI Evals configuration files for all evaluation scenarios **Success Criteria:** - [ ] Can run dot-ai evaluations using official OpenAI Evals CLI - [ ] Datasets validate against OpenAI Evals schema requirements - [ ] Results comparable between dot-ai and OpenAI Evals execution ### Milestone 4: MLflow & LangSmith Integration ⬜ **Target**: Export and import capabilities with major ML platforms **Key Deliverables:** - [ ] **MLflow Exporter**: Export experiments to MLflow tracking format - [ ] **LangSmith Exporter**: Export datasets and runs to LangSmith format - [ ] **Import Functionality**: Import experiments from external platforms - [ ] **Platform UI Integration**: View dot-ai results in MLflow/LangSmith UIs **Breaking Changes:** - [ ] **Add**: Export commands to evaluation CLI - [ ] **Integrate**: Platform-specific metadata in experiment records **Success Criteria:** - [ ] Can export dot-ai experiments to MLflow and view in MLflow UI - [ ] Can export to LangSmith and run comparative analyses - [ ] External teams can import and analyze dot-ai evaluation data ### Milestone 5: Reproducible Evaluation Environment ⬜ **Target**: Ensure evaluation results are reproducible across environments **Key Deliverables:** - [ ] **Environment Capture**: Record all relevant environment variables and versions - [ ] **Deterministic Execution**: Consistent results across different machines - [ ] **Versioned Datasets**: Dataset versioning with content hashing - [ ] **Experiment Replay**: Ability to exactly reproduce previous evaluations **Breaking Changes:** - [ ] **Add**: Environment metadata to all experiments - [ ] **Require**: Version pinning for reproducible evaluations **Success Criteria:** - [ ] Same evaluation produces identical results on different machines - [ ] Can reproduce historical evaluation results exactly - [ ] Environment differences are captured and reportable ### Milestone 6: Enhanced Reporting & Analytics ⬜ **Target**: Advanced analytics and reporting capabilities **Key Deliverables:** - [ ] **Statistical Reports**: Enhanced reports with significance testing results - [ ] **Trend Analysis**: Performance trends over time across model versions - [ ] **Interactive Dashboards**: Web-based evaluation result exploration - [ ] **Automated Insights**: AI-generated insights from evaluation patterns **Success Criteria:** - [ ] Reports include statistical significance and confidence intervals - [ ] Can track model performance trends over time - [ ] Interactive exploration of evaluation results ## Standards Compliance Validation ### OpenAI Evals Integration Testing - [ ] Install and run official OpenAI Evals framework - [ ] Execute dot-ai datasets using OpenAI Evals CLI - [ ] Validate results match dot-ai evaluation outcomes - [ ] Confirm all dataset formats pass OpenAI Evals validation ### MLflow Integration Testing - [ ] Export experiments to MLflow tracking server - [ ] View and analyze results in MLflow UI - [ ] Validate all metadata and metrics are preserved - [ ] Test experiment comparison functionality ### LangSmith Integration Testing - [ ] Export datasets to LangSmith platform - [ ] Run comparative evaluations in LangSmith - [ ] Validate dataset format compatibility - [ ] Test result import back to dot-ai system ## Risk Assessment ### High Impact Risks #### Risk: Standards Compatibility Complexity - **Probability**: Medium - **Impact**: High - **Mitigation**: Start with OpenAI Evals format first, expand gradually - **Owner**: AI Engineering Team #### Risk: Statistical Analysis Overhead - **Probability**: High - **Impact**: Medium - **Mitigation**: Implement optional statistical analysis, use efficient algorithms - **Owner**: Development Team ### Medium Impact Risks #### Risk: Breaking Changes to Existing Workflow - **Probability**: Medium - **Impact**: Medium - **Mitigation**: Maintain backward compatibility, phased migration - **Owner**: Development Team #### Risk: Platform Integration Complexity - **Probability**: Medium - **Impact**: Medium - **Mitigation**: Start with export-only, add import functionality later - **Owner**: Integration Team ## Resource Requirements ### Development Effort - **Total Estimate**: 8-12 weeks (1 senior developer) - **Standards Compliance Testing**: 2-3 weeks additional - **Platform Integration**: 3-4 weeks for each major platform ### Standards Validation Requirements - **OpenAI Evals Framework**: Install and validate against official framework - **MLflow Setup**: Test server deployment and experiment tracking - **LangSmith Access**: Platform access for integration testing - **Statistical Review**: Academic review of statistical methodology ## Success Criteria ### Functional Requirements - [ ] **OpenAI Evals Compatible**: All datasets and configs work with OpenAI Evals framework - [ ] **MLflow Integration**: Can export/import experiments to MLflow - [ ] **LangSmith Integration**: Can export/import datasets and runs to LangSmith - [ ] **Statistical Rigor**: All comparisons include significance testing and confidence intervals ### Quality Requirements - [ ] **Reproducibility**: All evaluations reproducible across environments - [ ] **Academic Standards**: Statistical methodology suitable for research publication - [ ] **Industry Credibility**: Framework recognized by ML evaluation community - [ ] **Backward Compatibility**: Existing evaluation workflow continues to work --- **Status**: Not Started - Spun out from PRD 154 Milestone 3 **Compliance**: OpenAI Evals, LangSmith, MLflow Standards **Dependencies**: PRD 154 evaluation framework (complete) **Owner**: AI Engineering Team **Created**: 2025-10-12