anishganguli shares a comprehensive technical guide to evaluating document processing pipelines using Microsoft AI services, covering everything from ground truth setup and OCR validation to precision-driven continuous improvement.

Evaluation Frameworks for Document Pipelines Using Azure AI & Search

Extracting structured data from large, semi-structured documents involves careful design and technical rigor. This guide, derived from a Tech Community blog by anishganguli, offers best practices for evaluating IDP (Intelligent Document Processing) pipelines using core Microsoft technologies: Azure Document Intelligence (ADI), Azure AI Search, and Azure OpenAI.

Why Rigorous Evaluation Matters

Mission-critical pipelines must be trusted to produce accurate, reliable, and scalable results. A robust evaluation framework divides the process into distinct phases, defines metrics, and ensures continuous improvement.

Stepwise Evaluation Framework

1. Establish Ground Truth & Sampling

Preparation: Create a reliable, manually annotated dataset to serve as the baseline for evaluation; involve domain experts (legal, finance) for accuracy.
Stratified Sampling: Test all document types and sections (e.g., contracts, annexes, tables) so metrics reflect the toughest content.
Automated Consensus: Use multiple automated “voters” (regex, ML models, logic checks) to measure extraction risk tiers before triggering human review. This balances manual workload and maintains quality.

2. Preprocessing Evaluation

OCR/Text Extraction: Measure character/error rates, confirm layout and reading order, verify complete sentence coverage—especially on multi-column or complex documents.
Chunking: Ensure chunk boundaries align with document structure; track completeness and segment integrity.
Multi-page Tables: Validate header handling for continued tables; minimize erroneous false headers.
Structural Links: Confirm footnotes, references, and anchors are accurately preserved; assess ontology/grouping coverage.

3. Labelling Evaluation

Section/Entity Accuracy: Treat chunk labelling as a classification problem; measure precision (correctness of chunk labels) and recall (coverage of true entities).
Actionable Insight: Low precision causes wrong data; low recall misses critical sections. Per-label metrics guide improvements.

4. Retrieval Evaluation

Precision@K: Top K retrieved chunks should be relevant; typically focus on ~3-5 for downstream extraction.
Recall@K: Track coverage for hard-to-find fields (e.g., in appendices).
Ranking Quality (MRR, NDCG): Measure if most relevant results appear early.
Trade-offs: Increasing K raises recall but may reduce precision—a balance tailored to domain risk.

5. Extraction Accuracy Evaluation

Field/Record Validation: Compare extracted values to ground truth; use strict and lenient matching for tangible accuracy reporting.
Error Analysis: Identify recurring mistakes (e.g., OCR issues, wrong retrievals, format errors) to drive fix prioritization.
Holistic Metrics: Report overall and per-field precision and recall; focus correction on high-priority fields.

6. Continuous Improvement Loop with SME

Iterative Refinement: Use each evaluation cycle’s errors to improve individual pipeline components.
A/B Testing and Monitoring: Deploy alternate methods for benchmarking and monitor production data for drift.
Generalization: Modular framework adapts across industries (legal, financial, healthcare, academic).

Key Takeaways for Practitioners

A phase-by-phase approach uncovers pipeline weaknesses early.
Combining automated signals and SME feedback scales manual review.
Precision/recall and ranking metrics deliver practical insights for ongoing tuning.
Best practice is continuous measurement, not “one and done”—metrics provide direction for every iteration.

Reference Implementation

For full code, architecture diagrams, and applied examples, see the original Tech Community blog: From Large Semi-Structured Docs to Actionable Data: Reusable Pipelines with ADI, AI Search & OpenAI

Author: anishganguli (Microsoft)

Last updated: Dec 15, 2025

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