AI Reliability Platform for LLM Evaluation
Built a full-stack system to monitor and control LLM generation at the token level, with adaptive instability detection and real-time intervention.
Key Insight
“Detect and correct unstable token generation in real time using entropy-based control.”
System Capabilities
- Token-by-token observability with probability and entropy extraction
- Real-time stability detection (entropy collapse, repetition loops, lock-in)
- Adaptive policy control with mid-generation intervention
- Confidence scoring with audit trail
Built a full-stack system to monitor and control LLM generation at the token level.
The system tracks entropy during decoding, detects instability patterns such as repetition loops and entropy collapse, and applies corrective actions (temperature adjustment, regeneration).
Includes: • Adaptive control loop (Token Distribution → Entropy Analysis → Policy Gate → Temperature/Regen Action) • Token-level observability (entropy + instability traces) • Confidence scoring based on generation stability • FastAPI backend + interactive UI dashboard • Support for 7B models (Mistral / Qwen via API)
Result: Prevents degeneration loops. Improves generation stability in real time by catching failure modes before they propagate.
Key Highlights
- Entropy-aware routing, adaptive regeneration policies, and behavioral workflows across 213 prompts spanning 5 behavioral categories
- 100% harmful-output catch rate on 43 adversarial prompts across 9 holdout rounds on fine-tuned mT5; extended testing to Qwen 2.5-7B, Llama 3 8B, and Mistral v3
- 9-category guardrail classifier with escalation logic — 0% leakage and 0% hallucination across 213 evals
- 55+ automated regression tests and CI-gated promotion workflows that blocked 7/7 unsafe model candidates
- Reduced behavioral fragility score 66% (25.0% → 8.5% stress-induced entropy spike under adversarial perturbation)
- Three-category stability detector system: entropy_collapse, repetition_loop, low_entropy_lock
- Confidence scoring weighted by entropy (50%), instability events (35%), regenerations (15%)
- 82% instability reduction and +0.07 average confidence improvement on adversarial prompts
- Interactive Next.js dashboard for baseline vs adaptive comparison with live metrics
Tech Stack
Core ML
Control Policy
Backend
Frontend
Infrastructure
Challenges
- Designing stability detectors that reliably trigger on actual failures without excessive false positives
- Managing mid-generation intervention policy without causing output quality degradation
- Computing token-level entropy efficiently during generation without performance bottlenecks
- Calibrating confidence metric weights to align with user perception of output reliability
- Handling model instability across diverse prompt domains without overfitting to training patterns
Key Learnings
- Token-level observability enables reliable failure detection — aggregate metrics alone miss early collapse signals
- Control policy specification must be explicit and formal — heuristic rules are more debuggable than learned policies for critical systems
- Confidence metrics are only useful if they accurately predict downstream failure — weighted scoring requires empirical validation
- Mid-generation intervention works but introduces latency trade-offs — cold-start regeneration may be more cost-effective than adaptive adjustment
- Interactive dashboards for compare mode are essential for understanding control system behavior — summary metrics alone don't build trust