Phase 5: Metrics & Monitoring - Track Resume Quality Indicators

[BPMSoftwareSolutions]

Phase 5: Metrics & Monitoring

Overview

Implement comprehensive metrics tracking and monitoring to measure resume quality, model performance, and system effectiveness across all phases.

Objectives

• Implement 6 core quality metrics
• Create metrics calculation pipeline
• Build metrics dashboard and reporting
• Demonstrate improved metrics through training phases

Deliverables

1. Coverage@Req Metric

• Create coverage calculator: src/metrics/coverage_metric.py
  • Extract JD must-haves (skills, technologies, experience)
  • Check if each requirement is addressed by at least one resume bullet
  • Calculate: % of JD must-haves covered

2. TruthScore Metric

• Create truth scorer: src/metrics/truth_score_metric.py
  • For each resume bullet, check if it maps to a retrieved snippet
  • Calculate: % of bullets with verifiable source

3. ImpactScore Metric

• Create impact scorer: src/metrics/impact_score_metric.py
  • Check each bullet for metrics/numbers (%, $, X%, improvement, etc.)
  • Calculate: % of bullets with quantified impact

4. Readability Metric

• Create readability scorer: src/metrics/readability_metric.py
  • Calculate Flesch-Kincaid grade level
  • Measure average sentence length
  • Check for jargon/complexity

5. ATS Keyword HitRate Metric

• Create ATS scorer: src/metrics/ats_metric.py
  • Extract keywords from JD
  • Check if keywords appear in resume
  • Calculate: % of JD keywords present in resume

6. Human Preference Metric (DPO)

• Create preference scorer: src/metrics/preference_metric.py
  • Track DPO preference accuracy
  • Calculate: % of times model chooses better variant

7. Integrated Metrics Pipeline

• Create metrics orchestrator: src/metrics/metrics_pipeline.py
  • Calculate all 6 metrics for a given resume
  • Aggregate into comprehensive report
  • Track metrics over time

8. Metrics Dashboard

• Create dashboard: scripts/metrics_dashboard.py
  • Display metrics over time (line charts)
  • Compare metrics across phases
  • Show top-performing resumes
  • Display metric trends
  • Export metrics to CSV/JSON

9. Metrics Tracking Database

• Create metrics store: src/metrics/metrics_store.py
  • Store metrics reports in data/metrics/
  • Query metrics by job/resume/phase
  • Calculate aggregates and trends
  • Support time-series analysis

10. Demonstration & Testing

• Create demo script: scripts/demo_metrics.py
  • Generate sample tailored resume
  • Calculate all 6 metrics
  • Display metrics report
  • Show metrics dashboard
  • Compare metrics across phases
• Create unit tests: tests/test_metrics.py
  • Test each metric calculation
  • Test metric accuracy
  • Test metrics aggregation
  • Test metrics storage and retrieval

Success Criteria

• All 6 metrics implemented and working
• Metrics calculations are accurate (validated manually)
• Metrics pipeline produces comprehensive reports
• Metrics improve across phases:
  • Phase 1 (RAG): +5-10% improvement
  • Phase 2 (LoRA): +10-15% improvement
  • Phase 3 (Critique): +5-10% improvement
  • Phase 4 (DPO): +5-10% improvement
• Dashboard displays metrics clearly
• Metrics stored and retrievable
• Demo shows clear metric improvements
• All unit tests pass

Files to Create/Modify

New Files:

• src/metrics/__init__.py
• src/metrics/coverage_metric.py
• src/metrics/truth_score_metric.py
• src/metrics/impact_score_metric.py
• src/metrics/readability_metric.py
• src/metrics/ats_metric.py
• src/metrics/preference_metric.py
• src/metrics/metrics_pipeline.py
• src/metrics/metrics_store.py
• scripts/metrics_dashboard.py
• scripts/demo_metrics.py
• tests/test_metrics.py
• data/metrics/.gitkeep

Modified Files:

• src/tailor.py - Integrate metrics tracking
• README.md - Document metrics

Related Issue

• Parent: Implement LLM Training Strategy: RAG + LoRA Fine-Tuning for Agentic Resume Tailoring #45 - Implement LLM Training Strategy: RAG + LoRA Fine-Tuning
• Depends on: Phase 1: RAG Integration - Index and Retrieve Experience Snippets #46 (Phase 1), Phase 2: LoRA Fine-Tuning - Train on Curated Resume Tailoring Pairs #47 (Phase 2), Phase 3: Critique/Verifier Agent - Truth Filters and Self-Critique Loop #48 (Phase 3), Phase 4: DPO Preference Optimization - Rank and Learn from Resume Variants #49 (Phase 4)

Acceptance Criteria

1. ✅ All 6 metrics implemented and accurate
2. ✅ Metrics pipeline produces comprehensive reports
3. ✅ Metrics improve across all phases
4. ✅ Dashboard displays metrics clearly
5. ✅ Metrics stored and retrievable
6. ✅ Demo shows clear metric improvements
7. ✅ All tests pass
8. ✅ Documentation updated with metrics examples