Manual reviews were the chokepoint: costly, inconsistent, and impossible to scale to thousands of weekly submissions. Traditional graders captured functional correctness but missed readability, maintainability, and algorithmic efficiency, forcing human review back into the loop for quality—and creating delays.

Constraints

• Limited expert bandwidth; variable non-expert quality
• Need for explainability and repeatability
• Tight SLAs across high-volume pipelines

Human-in-the-loop by design—a partial-autonomy system that routes only high-confidence cases to automation and escalates ambiguity to people.

What we built

• Rubric-based LLM scoring: Structured judgments for clarity, readability/maintainability, and algorithmic efficiency.
• Expert-aligned classifier: Gradient boosting combines LLM rubric features with quantitative signals (e.g., test-pass rate, difficulty, language) to predict auto-pass / auto-reject / needs-review.
• Tiered review workflow: Ambiguous cases go to trained reviewers; sampled expert audits build a golden set to calibrate models and humans.
• Continuous improvement loop: Disagreements drive retraining, reviewer coaching, and threshold updates.

1. Ingest: Code + metadata (tests, language, problem ID, difficulty)
2. LLM rubric layer: Structured scores for clarity, readability, complexity
3. Classifier: Gradient-boosted model produces decision + confidence
4. Orchestration:
5. Auto-pass / auto-reject → instant disposition
6. Needs review → routed to reviewers (non-expert), then expert sampling
7. Governance: Expert audits, drift monitoring, and KPI dashboards (coverage, precision, cycle time, unit cost)

• 85% of submissions auto-assessed with confidence
• 90% agreement with experts in auto zones
• Up to 80% reduction in cost per decision
• Manual review time improved from ~40 → ~30 minutes for non-experts through workflow tuning
• Candidate progression time: instant for auto zones; ~5.2 days average for human-review cases (down from 15)

• Coverage: From ~50% sampled reviews to ~100% assessed
• Throughput: Backlog eliminated; hiring velocity increased
• Quality: Goverened alignment via expert audits and golden sets
• Analytics: Portfolio-level insights across problems, languages, and difficulty

Define your path to human-in-the-loop automation and Proprietary Intelligence across high-volume decisions.

Talk to a Turing Strategist