# PRD 154: AI Evaluation Framework for Quality Assessment and Optimization
## Overview
**Problem Statement**: The dot-ai toolkit has 6+ AI-powered tools with no systematic way to measure output quality, detect performance regressions, or make data-driven decisions for model/provider selection using industry-standard evaluation practices.
**Solution**: Build a comprehensive AI evaluation framework following OpenAI Evals and LangSmith standards, providing automated quality assessment, regression detection, and performance optimization capabilities across all AI-powered tools.
**Success Metrics**:
- 95% of AI interactions evaluated using standard eval formats
- 50% reduction in undetected AI performance regressions
- 20-40% cost optimization through data-driven provider selection
- Full compliance with OpenAI Evals and LangSmith standards
## Strategic Context
### Business Impact
- **Industry Credibility**: First DevOps AI toolkit following established eval standards
- **Cost Optimization**: Data-driven provider selection reducing AI costs by 20-40%
- **Quality Assurance**: Systematic quality measurement and regression prevention
- **Research Collaboration**: Enable academic partnerships and benchmark contributions
### User Impact
- **Transparency**: Standard evaluation reports users can trust and understand
- **Reliability**: Consistent, measurable AI quality across all tools
- **Performance**: Optimal speed/quality balance based on industry-standard metrics
- **Benchmarking**: Compare dot-ai performance against other AI systems
## Standard Framework Analysis
### OpenAI Evals Standard
**Core Components:**
```jsonl
// Standard dataset format (samples.jsonl)
{"input": "Deploy PostgreSQL database", "ideal": "Use StatefulSet with persistent storage", "metadata": {"category": "database", "complexity": "medium"}}
// Standard evaluation config
{
"eval_name": "dot-ai-kubernetes-deployment",
"eval_spec": {
"cls": "evals.elsuite.basic.match:Match",
"args": {"samples_jsonl": "k8s-deployment.jsonl"}
},
"completion_fns": ["anthropic/claude-sonnet", "openai/gpt-4"]
}
```
### LangSmith Standard
**Evaluation Structure:**
```typescript
interface LangSmithEval {
dataset_name: string;
evaluators: Evaluator[];
experiment_prefix: string;
metadata: {
version: string;
description: string;
tags: string[];
};
}
```
### Industry Best Practices
1. **Standardized Datasets**: JSONL format with input/ideal/metadata structure
2. **Evaluation Metrics**: Named evaluators (correctness, relevance, safety, etc.)
3. **Reproducible Results**: Deterministic scoring with confidence intervals
4. **Comparative Analysis**: Multi-model evaluation with statistical significance
5. **Versioned Experiments**: Track evaluation changes over time
## Technical Architecture (Standards-Compliant)
### 1. Standard Dataset Format
```typescript
// Replace current ad-hoc test cases with standard format
interface StandardEvalSample {
input: {
prompt: string;
context?: Record<string, any>;
};
ideal: string | string[]; // Expected output(s)
metadata: {
category: string; // "deployment" | "remediation" | "testing"
complexity: "low" | "medium" | "high";
tags: string[];
source: string; // Where this sample came from
};
}
// Example datasets:
// eval-datasets/kubernetes-deployment.jsonl
// eval-datasets/troubleshooting-remediation.jsonl
// eval-datasets/documentation-testing.jsonl
```
### 2. Standard Evaluators
```typescript
// Follow LangSmith evaluator pattern
interface StandardEvaluator {
name: string;
description: string;
evaluate(input: any, output: string, ideal?: string): Promise<EvaluationScore>;
}
class CorrectnessEvaluator implements StandardEvaluator {
name = "correctness";
description = "Measures factual accuracy of AI output";
async evaluate(input: any, output: string, ideal: string): Promise<EvaluationScore> {
// Use model-graded evaluation following OpenAI pattern
const score = await this.aiProvider.evaluate({
criteria: "factual accuracy",
input, output, ideal
});
return {
key: "correctness",
score: score,
comment: "Evaluation reasoning...",
confidence: 0.9
};
}
}
```
### 3. Standard Experiment Tracking
```typescript
// Follow MLflow/LangSmith experiment structure
interface EvaluationExperiment {
experiment_id: string;
name: string;
description: string;
dataset_name: string;
model: string;
evaluators: string[];
results: {
summary: {
total_samples: number;
avg_score: number;
scores_by_evaluator: Record<string, number>;
cost_usd: number;
duration_seconds: number;
};
samples: Array<{
sample_id: string;
input: any;
output: string;
ideal?: string;
scores: Record<string, EvaluationScore>;
metadata: Record<string, any>;
}>;
};
created_at: string;
tags: string[];
}
```
## Implementation Plan (Standards-First)
### Milestone 1: Standard Dataset Creation & Core Framework ⬜
**Target**: Create standard evaluation datasets and core evaluation engine
**Key Deliverables:**
- [ ] **Refactor Integration Tests**: Convert existing integration tests to standard JSONL datasets
- [ ] **Standard Evaluator Framework**: Implement OpenAI Evals-compatible evaluator interface
- [ ] **Core Evaluation Engine**: Build evaluation runner following LangSmith patterns
- [ ] **Dataset Creation**: 50+ samples across kubernetes-deployment, troubleshooting, documentation domains
**Breaking Changes:**
- [ ] **Remove**: `src/core/providers/provider-debug-utils.ts` custom metrics format
- [ ] **Replace**: Ad-hoc test validation with standard evaluator scoring
- [ ] **Restructure**: `tests/integration/` to generate standard eval datasets
**Success Criteria:**
- [ ] All evaluation datasets in standard JSONL format
- [ ] Evaluation results compatible with OpenAI Evals format
- [ ] Can reproduce evaluations using standard tooling
**Documentation Updates:**
- [ ] `docs/evaluation-standards.md`: Framework overview following industry standards
- [ ] `docs/dataset-creation.md`: Guidelines for creating standard datasets
### Milestone 2: Standard Evaluators & Model-Graded Evaluation ⬜
**Target**: Implement full suite of standard evaluators with model-graded scoring
**Key Deliverables:**
- [ ] **Standard Evaluator Suite**: Correctness, Relevance, Safety, Completeness, Consistency evaluators
- [ ] **Model-Graded Infrastructure**: Use GPT-4/Claude Opus for subjective evaluation following OpenAI patterns
- [ ] **Confidence Intervals**: Statistical confidence measurement for all scores
- [ ] **Cross-Validation**: Multiple runs with statistical significance testing
**Breaking Changes:**
- [ ] **Replace**: Custom quality metrics with standard evaluator scores
- [ ] **Standardize**: All evaluation outputs to use standard schema
**Success Criteria:**
- [ ] Model-graded evaluation achieves >85% correlation with human judgment
- [ ] All evaluators produce statistically significant results
- [ ] Evaluation results exportable to standard analysis tools (MLflow, Weights & Biases)
**Documentation Updates:**
- [ ] `docs/evaluation-standards.md`: Complete evaluator documentation
- [ ] `docs/model-graded-evaluation.md`: Model-graded evaluation methodology
### Milestone 3: Standard Experiment Tracking & Comparison ⬜
**Target**: Full experiment tracking system compatible with industry standards
**Key Deliverables:**
- [ ] **Experiment Management**: Track evaluation runs with standard metadata
- [ ] **Multi-Provider Comparison**: Standardized comparison across Anthropic, OpenAI, Google
- [ ] **Statistical Analysis**: Significance testing, confidence intervals, effect sizes
- [ ] **Standard Export**: Export to MLflow, LangSmith, Weights & Biases formats
**Breaking Changes:**
- [ ] **Replace**: Current JSONL metrics with standard experiment format
- [ ] **Restructure**: All evaluation data storage to use standard schema
**Success Criteria:**
- [ ] Can import/export experiments to/from standard ML platforms
- [ ] Statistical significance testing for all model comparisons
- [ ] Reproducible evaluation results across different environments
**Documentation Updates:**
- [ ] `docs/evaluation-standards.md`: Experiment tracking and analysis
- [ ] `docs/provider-comparison.md`: Standard comparison methodology
### Milestone 4: Standard Prompt Evaluation & Optimization ⬜
**Target**: Systematic prompt evaluation following industry best practices
**Key Deliverables:**
- [ ] **Prompt Versioning**: Track prompt changes with evaluation impact
- [ ] **A/B Testing Framework**: Standard statistical testing for prompt variations
- [ ] **Prompt Regression Detection**: Automated detection using statistical significance
- [ ] **Optimization Pipeline**: Systematic prompt improvement using standard metrics
**Breaking Changes:**
- [ ] **Add**: Evaluation metadata to all prompt files
- [ ] **Restructure**: Prompt evaluation to use standard dataset/evaluator pattern
**Success Criteria:**
- [ ] All prompt changes evaluated using standard statistical methods
- [ ] Prompt optimization shows measurable improvement in standard metrics
- [ ] Prompt evaluation results compatible with academic research standards
**Documentation Updates:**
- [ ] `docs/prompt-evaluation.md`: Standard prompt evaluation methodology
- [ ] `docs/prompt-optimization.md`: Systematic prompt improvement process
### Milestone 5: CI/CD Integration & Production Monitoring ⬜
**Target**: Full integration with development workflow using standard practices
**Key Deliverables:**
- [ ] **Standard CI/CD Integration**: Evaluation gates following MLOps best practices
- [ ] **Regression Detection**: Statistical significance testing for quality changes
- [ ] **Production Monitoring**: Continuous evaluation using standard sampling methods
- [ ] **Reporting Dashboard**: Standard evaluation reporting and analytics
**Breaking Changes:**
- [ ] **Replace**: Custom debug logging with standard evaluation monitoring
- [ ] **Integrate**: Standard evaluation gates in CI/CD pipeline
**Success Criteria:**
- [ ] All AI changes evaluated using statistical significance testing
- [ ] Production AI quality monitored using industry-standard methods
- [ ] Evaluation results usable for academic publication and benchmarking
**Documentation Updates:**
- [ ] `docs/evaluation-standards.md`: Complete CI/CD and monitoring integration
- [ ] `README.md`: Update with standard evaluation capabilities and compliance
## Standard Dataset Design
### Kubernetes Deployment Evaluation
```jsonl
{"input": {"intent": "deploy postgresql database", "cluster_context": "3 nodes, default storage"}, "ideal": "StatefulSet with PersistentVolumeClaims for data persistence", "metadata": {"category": "database", "complexity": "medium", "tags": ["stateful", "persistence"], "source": "integration_test"}}
{"input": {"intent": "deploy web application", "cluster_context": "production cluster"}, "ideal": "Deployment with Service and Ingress for external access", "metadata": {"category": "web_app", "complexity": "low", "tags": ["stateless", "external"], "source": "user_scenario"}}
```
### Troubleshooting Remediation Evaluation
```jsonl
{"input": {"issue": "pods stuck in pending state", "cluster_info": "3 worker nodes, default scheduler"}, "ideal": ["kubectl describe pod", "kubectl get nodes", "check resource limits"], "metadata": {"category": "scheduling", "complexity": "medium", "tags": ["resources", "scheduling"], "source": "common_issue"}}
{"input": {"issue": "service not accessible", "cluster_info": "ingress controller deployed"}, "ideal": ["kubectl get svc", "kubectl describe ingress", "check endpoint connectivity"], "metadata": {"category": "networking", "complexity": "high", "tags": ["networking", "ingress"], "source": "production_issue"}}
```
### Documentation Testing Evaluation
```jsonl
{"input": {"doc_content": "Run kubectl apply -f deployment.yaml", "context": "deployment guide"}, "ideal": "Valid YAML file exists and command succeeds", "metadata": {"category": "command_validation", "complexity": "low", "tags": ["kubectl", "deployment"], "source": "doc_analysis"}}
```
## Standard Evaluator Implementation
### Following OpenAI Evals Pattern
```typescript
// Standard evaluator base class
abstract class StandardEvaluator {
abstract name: string;
abstract description: string;
abstract async evaluate(
input: any,
output: string,
ideal?: string
): Promise<EvaluationScore>;
// Standard confidence calculation
calculateConfidence(scores: number[]): number {
// Standard deviation based confidence
const mean = scores.reduce((a, b) => a + b) / scores.length;
const std = Math.sqrt(scores.reduce((sq, n) => sq + (n - mean) ** 2) / scores.length);
return Math.max(0, Math.min(1, 1 - (std / mean)));
}
}
// Kubernetes-specific evaluators following standard pattern
class KubernetesCorrectnessEvaluator extends StandardEvaluator {
name = "k8s_correctness";
description = "Evaluates correctness of Kubernetes recommendations";
async evaluate(input: any, output: string, ideal: string): Promise<EvaluationScore> {
// Use model-graded evaluation with K8s expertise
const gradingPrompt = `
You are evaluating a Kubernetes recommendation for correctness.
Input: ${JSON.stringify(input)}
AI Output: ${output}
Expected: ${ideal}
Rate 0-1 how correct the AI output is compared to the expected answer.
Consider: resource types, configuration accuracy, best practices.
Return only a number between 0 and 1.
`;
const response = await this.aiProvider.sendMessage(gradingPrompt);
const score = parseFloat(response.content) || 0;
return {
key: this.name,
score: score,
comment: `Kubernetes correctness evaluation`,
confidence: 0.9
};
}
}
```
## Breaking Changes Summary
### Files to Remove/Replace
- [ ] `src/core/providers/provider-debug-utils.ts` → Replace with standard evaluation framework
- [ ] Custom metrics format → Replace with standard experiment tracking
- [ ] Ad-hoc test validation → Replace with standard evaluators
### New Standard-Compliant Structure
```
eval-datasets/ # Standard JSONL datasets
kubernetes-deployment.jsonl
troubleshooting-remediation.jsonl
documentation-testing.jsonl
src/evaluation/ # Standard evaluation framework
evaluators/ # Standard evaluator implementations
correctness.ts
relevance.ts
safety.ts
completeness.ts
experiments/ # Experiment tracking
manager.ts
schema.ts
datasets/ # Dataset management
loader.ts
validator.ts
eval-configs/ # OpenAI Evals compatible configs
kubernetes-deployment.yaml
troubleshooting.yaml
```
## Success Criteria (Standards Compliance)
### Functional Requirements
- [ ] **OpenAI Evals Compatible**: All datasets and configs work with OpenAI Evals framework
- [ ] **LangSmith Integration**: Can export/import experiments to LangSmith
- [ ] **MLflow Compatible**: Experiment tracking follows MLflow standards
- [ ] **Statistical Rigor**: All comparisons include significance testing and confidence intervals
### Quality Requirements
- [ ] **Reproducibility**: All evaluations reproducible across environments
- [ ] **Academic Standards**: Evaluation methodology suitable for research publication
- [ ] **Industry Benchmarks**: Can participate in industry AI evaluation benchmarks
- [ ] **Community Contribution**: Datasets and methods sharable with research community
## Risk Assessment
### High Impact Risks
**Risk: Standards Compliance Complexity**
- **Probability**: Medium
- **Impact**: High
- **Mitigation**: Start with OpenAI Evals format first, expand gradually
- **Owner**: AI Engineering Team
**Risk: Evaluation Cost with Standards**
- **Probability**: High
- **Impact**: Medium
- **Mitigation**: Smart sampling strategies, use cheaper models for bulk evaluation
- **Owner**: Product Team
### Medium Impact Risks
**Risk: Integration Effort**
- **Probability**: Medium
- **Impact**: Medium
- **Mitigation**: Phased approach, maintain existing functionality during transition
- **Owner**: Development Team
## Resource Requirements
### Development Effort
- **Total Estimate**: 10-14 weeks (1 senior developer)
- **Additional**: 2 weeks for standard compliance validation
- **Refactoring**: 3-4 weeks for breaking changes
### Standards Compliance Validation
- **OpenAI Evals Integration**: Test with actual OpenAI Evals framework
- **LangSmith Export**: Validate full experiment export/import
- **Statistical Validation**: Academic review of methodology
---
**Status**: Draft - Standards-First Approach
**Compliance**: OpenAI Evals, LangSmith, MLflow Standards
**Next Review**: After standards validation and team approval
**Owner**: AI Engineering Team
**Last Updated**: 2025-01-15
**Breaking Changes**: Yes - Full refactor to industry standards
