AI Evaluations Pipeline

09 June 2025 - 4 mins read time
Tags: Coding AI

AI Evaluation Pipeline Study Guide

Overview

AI application success depends on the ability to differentiate good outcomes from bad outcomes. This requires a reliable evaluation pipeline. This guide covers comprehensive evaluation techniques for open-ended AI tasks.

Step 1: Evaluate All System Components

Multi-Level Evaluation

• Component-level: Evaluate each system component independently
• End-to-end: Evaluate complete system output
• Turn-based: Evaluate quality of individual responses
• Task-based: Evaluate whether system completes entire tasks

Example: Resume Parser Application

1. PDF-to-text extraction - Evaluate using text similarity
2. Employer extraction - Evaluate using accuracy metrics

Turn vs Task Evaluation

• Turn-based: Quality of each individual output
• Task-based: Whether system accomplishes the goal (more important for users)
• Challenge: Determining task boundaries in conversations

Benchmark Example

• Twenty Questions: One AI instance chooses concept, another guesses through yes/no questions
• Scored on success rate and number of questions needed

Step 2: Create Evaluation Guidelines

Define What Good Means

• Specify what application should do
• Specify what application shouldn’t do
• Define scope boundaries and out-of-scope responses

Evaluation Criteria Development

Average applications use 2.3 different feedback criteria:

Example Customer Support Criteria:

1. Relevance: Response addresses user’s query
2. Factual Consistency: Response aligns with context
3. Safety: Response isn’t toxic

Create Scoring Rubrics

• Choose scoring system (binary, 1-5 scale, 0-1 range)
• Provide examples for each score level
• Validate rubrics with human reviewers
• Ensure unambiguous guidelines

Business Metric Alignment

Map evaluation metrics to business impact:

• 80% factual consistency → automate 30% of support requests
• 90% factual consistency → automate 50% of support requests
• 98% factual consistency → automate 90% of support requests

Key Business Metrics:

• Stickiness: DAU, WAU, MAU
• Engagement: Conversations per month, session duration

Step 3: Define Methods and Data

Select Evaluation Methods

• Specialized classifiers for toxicity detection
• Semantic similarity for relevance measurement
• AI judges for factual consistency
• Mixed approaches for cost/quality balance

Leverage Logprobs

• Measure model confidence in predictions
• Useful for classification tasks
• Calculate perplexity for fluency assessment

Human Evaluation

• Use as North Star metric
• Evaluate subset of daily outputs (e.g., 500 conversations)
• Detect performance changes and usage patterns

Data Annotation Strategy

• Use actual production data when possible
• Leverage natural labels if available
• Create clear annotation guidelines
• Reuse guidelines for future fine-tuning

Data Slicing Techniques

Separate data into subsets for granular analysis:

• Bias detection: Avoid discrimination against minority groups
• Debugging: Identify performance issues in specific data types
• Improvement opportunities: Find areas needing enhancement
• Simpson’s paradox prevention: Avoid aggregate score misleading

Evaluation Set Types

• Production representative: Matches actual usage distribution
• User tier based: Paying vs free users
• Platform based: Mobile vs web traffic
• Error-prone examples: Known failure cases
• Typo-containing: Common user input errors
• Out-of-scope: Inappropriate inputs

Sample Size Guidelines

• Minimum: 300 examples (absolute minimum)
• Preferred: 1,000+ examples
• Benchmark median: 1,000 examples
• Benchmark average: 2,159 examples

Score Difference vs Sample Size (OpenAI guidelines):

• For every 3× decrease in score difference, need 10× more samples

Bootstrap Validation

1. Draw samples with replacement from evaluation set
2. Evaluate model on bootstrapped samples
3. Repeat multiple times
4. Check for consistent results across bootstraps

Evaluation Pipeline Quality Assessment

Key Quality Questions

• Do better responses receive higher scores?
• Do better metrics correlate with business outcomes?
• Is the pipeline reproducible across runs?
• What’s the variance across different datasets?

Reliability Improvements

• Set consistent configurations (e.g., temperature = 0 for AI judges)
• Track metric correlations
• Remove redundant perfectly correlated metrics
• Investigate uncorrelated metrics

Cost and Latency Considerations

• Balance evaluation thoroughness with performance
• Don’t skip evaluation to reduce latency
• Consider async evaluation where possible

Iteration and Maintenance

Continuous Improvement

• Update criteria as needs change
• Modify scoring rubrics based on learnings
• Add/remove examples as patterns emerge
• Maintain consistency while evolving

Experiment Tracking

Log all variables that could affect evaluation:

• Evaluation data versions
• Scoring rubrics
• AI judge prompts and configurations
• Sampling parameters

Model Selection Considerations

Host vs API Decision Factors

Evaluate across seven axes:

1. Data Privacy: Control over sensitive information
2. Data Lineage: Tracking data flow and usage
3. Performance: Speed and reliability requirements
4. Functionality: Feature availability and limitations
5. Control: Customization and configuration options
6. Cost: Total cost of ownership
7. Maintenance: Ongoing operational requirements

Public Benchmark Limitations

• Help eliminate bad models but don’t identify best for specific use cases
• Likely contaminated with training data
• Aggregation methodologies often unclear
• Should complement, not replace, private evaluation

Key Takeaways

1. No perfect evaluation method exists - combine multiple approaches
2. Evaluation is ongoing - continue throughout development and production
3. Clear guidelines are crucial - ambiguous criteria lead to unreliable results
4. Business alignment matters - connect evaluation metrics to business outcomes
5. Component-level evaluation - test each system part independently
6. User feedback integration - leverage production user interactions
7. Continuous iteration - evolve evaluation as requirements change

This evaluation pipeline framework provides the foundation for reliable AI system assessment, enabling risk reduction, performance improvement opportunities, and progress benchmarking throughout the development lifecycle.