Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation

xies miss, all while reducing human annotation requirements by an order of magnitude.

Core Solution

Implementing PPI in production requires a structured workflow that integrates prediction generation, intelligent sampling, and debiased estimation. The GLIDE library provides a scipy-style API designed for mean estimation, allowing seamless integration into existing evaluation pipelines. The implementation involves three primary phases: prediction collection, ground truth sampling, and statistical estimation.

Architecture Decisions

1. Unified API Design: GLIDE abstracts the mathematical complexity of PPI variants behind a consistent interface. This allows teams to swap estimators (e.g., from Predict-Then-Debias to PPI++) without refactoring the evaluation logic.
2. Sampler Specialization: The library supports multiple sampling strategies, including uniform, stratified, active, and cost-optimal samplers. Stratified sampling is recommended when the dataset contains heterogeneous subgroups (e.g., task types or complexity levels) that correlate with prediction error. Active sampling is optimal when annotation costs vary or when the goal is to minimize sample size for a target precision.
3. Decision Support: GLIDE ships with a decision tree that recommends estimators and samplers based on dataset characteristics, such as prediction bias magnitude, variance structure, and available annotation budget.

Implementation Example

The following TypeScript-style pseudocode (adapted for Python implementation patterns) demonstrates a production-grade PPI evaluation pipeline using GLIDE. This example evaluates an agentic system's task completion rate, leveraging stratified sampling to handle varying task complexities.

import glide
from glide.samplers import StratifiedSampler, CostOptimalSampler
from glide.estimators import PPIPlusPlus, ActiveStatisticalInference
from glide.validation import MonteCarloValidator

class AgenticEvaluator:
    def __init__(self, llm_judge, ground_truth_store):
        self.judge = llm_judge
        self.store = ground_truth_store

    def evaluate_trajectory_batch(self, trajectories, annotation_budget=0.05):
        # Phase 1: Generate predictions for the full dataset
        predictions = self.judge.score_batch(trajectories)
        
        # Phase 2: Select ground truth sample using stratified sampling
        # Strata are defined by task complexity to reduce variance
        sampler = StratifiedSampler(
            strata_key="task_complexity",
            allocation="proportional"
        )
        sample_indices = sampler.select(
            population_size=len(trajectories),
            budget=annotation_budget
        )
        
        # Fetch ground truth for the selected sample
        ground_truth = self.store.fetch_scores(sample_indices)
        
        # Phase 3: Compute debiased estimate with valid confidence interval
        estimator = PPIPlusPlus(alpha=0.05)
        result = estimator.estimate(
            predictions=predictions,
            sample_indices=sample_indices,
            ground_truth=ground_truth
        )
        
        return {
            "debiased_mean": result.mean,
            "confidence_interval": result.ci,
            "sample_size": len(sample_indices),
            "bias_correction": result.bias_estimate
        }

    def optimize_annotation_cost(self, trajectories, target_precision=0.02):
        # Use Active Statistical Inference to minimize sample size
        sampler = CostOptimalSampler(target_precision=target_precision)
        estimator = ActiveStatisticalInference(alpha=0.05)
        
        # Active sampling iteratively selects samples based on uncertainty
        result = estimator.estimate_active(
            predictions=self.judge.score_batch(trajectories),
            sampler=sampler,
            fetch_gt_fn=self.store.fetch_scores
        )
        
        return result

Rationale for Choices

• Stratified Sampling: Agentic tasks often exhibit high variance across different categories (e.g., code generation vs. data extraction). Stratification ensures that each subgroup is adequately represented in the ground truth sample, reducing the variance of the estimator and narrowing confidence intervals without increasing annotation cost.
• PPI++ Estimator: This estimator provides robust bias correction even when predictions are highly biased, as long as the bias is consistent across the sample. It is the default recommendation for most production scenarios due to its stability.
• Active Statistical Inference: When annotation costs are non-uniform or when the goal is to achieve a specific precision threshold with minimal labels, active sampling adaptively selects the most informative samples. This is particularly valuable in agentic evaluation where some trajectories are harder to verify than others.
• Monte Carlo Validation: GLIDE includes a validation suite that simulates evaluation scenarios to verify estimator performance. Teams should use this to benchmark their pipeline before deploying to production, ensuring that confidence intervals maintain nominal coverage.

Pitfall Guide

Implementing PPI in production requires careful attention to statistical assumptions and pipeline design. The following pitfalls are common in early adoption and can compromise evaluation validity.

1. Ignoring Prediction Correlation

   • Explanation: PPI estimators rely on the correlation between predictions and ground truth to reduce variance. If predictions are uncorrelated with the true metric, the estimator may exhibit high variance, negating the benefits of PPI.
   • Fix: Always compute the Pearson correlation between predictions and a small pilot ground truth sample. If correlation is below 0.3, consider improving the LLM judge or using a simpler estimator like Predict-Then-Debias.

2. Stratification Mismatch

   • Explanation: Stratified sampling only reduces variance if the strata correlate with the outcome variable. Using irrelevant strata (e.g., stratifying by user ID when evaluating task completion) can increase variance or provide no benefit.
   • Fix: Validate strata relevance by analyzing prediction error distribution across strata. Use domain knowledge to define strata that capture heterogeneity in task difficulty or agent behavior.

3. Data Leakage Between Prediction and Ground Truth

   • Explanation: PPI assumes that predictions are fixed or independent of the ground truth sample. If the LLM judge is trained or prompted using the same data points selected for ground truth, the independence assumption is violated, leading to biased estimates.
   • Fix: Ensure that the prediction generation step is completed before sampling ground truth. Use a hold-out set for judge calibration that is disjoint from the evaluation dataset.

4. Misinterpreting Confidence Interval Width

   • Explanation: Teams often focus solely on CI width as a measure of precision, overlooking the bias correction component. A narrow CI with insufficient bias correction can still yield misleading point estimates.
   • Fix: Report both the debiased mean and the bias correction magnitude. Use GLIDE's decision tree to select estimators that balance bias reduction and variance based on prediction quality.

5. Overusing Active Sampling

   • Explanation: Active Statistical Inference is powerful but computationally intensive and requires iterative querying. In scenarios with uniform annotation costs and low variance, active sampling may not outperform stratified sampling.
   • Fix: Use active sampling only when annotation costs vary significantly or when target precision is stringent. For standard evaluations, stratified PPI++ is often more efficient.

6. Neglecting Monte Carlo Validation

   • Explanation: PPI methods have asymptotic guarantees that may not hold for small sample sizes or highly skewed distributions. Deploying without validation can result in under-coverage of confidence intervals.
   • Fix: Run GLIDE's Monte Carlo validation suite on a representative subset of your data. Verify that empirical coverage matches the nominal confidence level (e.g., 95%).

7. Static Budget Allocation

   • Explanation: Fixing the annotation budget without considering dataset size or variance can lead to under-sampling in large datasets or over-sampling in small ones.
   • Fix: Use GLIDE's cost-optimal sampler to dynamically determine the minimum sample size required for a target precision. Adjust budgets based on pilot studies and variance estimates.

Production Bundle

Action Checklist

• Define Evaluation Metric: Specify the mean metric to estimate (e.g., task completion rate, reward score) and ensure it is bounded or transformable.
• Generate Predictions: Run the LLM-as-judge on the full dataset to obtain predictions. Validate prediction quality with a small pilot ground truth sample.
• Analyze Variance Structure: Inspect prediction distribution and error patterns. Identify potential strata for stratified sampling.
• Select Sampler and Estimator: Use GLIDE's decision tree to choose the appropriate sampler (uniform, stratified, active) and estimator (PPI++, Predict-Then-Debias, etc.).
• Run Monte Carlo Validation: Execute GLIDE's validation suite to confirm estimator performance and CI coverage on your data distribution.
• Execute PPI Pipeline: Sample ground truth, fetch labels, and compute the debiased estimate with confidence interval.
• Document Results: Record the debiased mean, CI width, sample size, bias correction, and annotation cost. Compare against baseline metrics.
• Iterate on Judge Quality: If variance is high or bias correction is large, refine the LLM judge prompts or architecture and re-evaluate.

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
High prediction bias, known strata	Stratified PPI++	Stratification reduces variance; PPI++ corrects bias robustly.	Low annotation cost; moderate compute.
Unknown strata, moderate bias	PPI++ with Uniform Sampling	PPI++ handles bias without requiring strata definition.	Low annotation cost; low compute.
Variable annotation costs, strict precision	Active Statistical Inference	Adaptive sampling minimizes sample size for target precision.	Lowest annotation cost; high compute.
Low bias, high variance predictions	Predict-Then-Debias	Simpler estimator sufficient when bias is minimal.	Low annotation cost; low compute.
Small dataset (<1k samples)	Stratified PPI with Pilot Validation	Ensures CI validity in finite samples; pilot validates assumptions.	Moderate annotation cost; low compute.

Configuration Template

The following YAML configuration demonstrates how to parameterize a GLIDE evaluation pipeline for an agentic system. This template can be integrated into CI/CD workflows to automate evaluation.

evaluation:
  metric: task_completion_rate
  confidence_level: 0.95
  
  predictions:
    source: llm_judge_v2
    batch_size: 100
    cache: true
    
  sampling:
    strategy: stratified
    strata_key: task_category
    allocation: proportional
    budget_fraction: 0.05
    
  estimator:
    method: ppi_plus_plus
    bias_correction: true
    variance_reduction: true
    
  validation:
    monte_carlo_runs: 1000
    coverage_threshold: 0.94
    
  output:
    format: json
    include_bias_estimate: true
    artifact_path: /results/evaluation_run_2024

Quick Start Guide

1. Install GLIDE: Run pip install glide-ppi to install the library and its dependencies.
2. Prepare Data: Generate predictions for your dataset using your LLM judge. Store predictions and metadata (e.g., strata keys) in a structured format.
3. Run Estimator: Use the GLIDE API to select a sample, fetch ground truth, and compute the debiased estimate. Example:

   import glide
   result = glide.estimate(
       predictions=predictions,
       sampler=glide.samplers.StratifiedSampler(strata_key="category"),
       estimator=glide.estimators.PPIPlusPlus(alpha=0.05)
   )
   print(f"Debiased Mean: {result.mean}, CI: {result.ci}")
   

4. Validate and Deploy: Run the Monte Carlo validation suite to verify CI coverage. Integrate the pipeline into your evaluation workflow and monitor annotation costs and precision metrics over time.