LLMGuard catches hallucination regressions between model checkpoints before you ship. The only tool that catches confident hallucinations — where the model sounds completely certain but is wrong.
From token-level probability extraction to structured regression reports — a complete hallucination testing pipeline.
Compare v14 vs v15 across 1,000 prompts in under 15 minutes. Get a signed delta with 95% confidence intervals, domain breakdown, and a clear deploy/block recommendation.
The only tool that catches hallucinations where the model sounds completely certain. High Top-1 probability, low entropy — but TTM's temporal analysis reveals the subtle instability signature.
IBM TTM analyzes a numerical feature matrix, not raw text. Under 10MB model, CPU-deployable, under 5ms inference. Full SDK round-trip in under 20ms at p99.
Only numerical feature matrices leave your environment — never prompt text, completion text, or raw logprobs. Open-source SDK is auditable. Works with on-premises deployment for regulated industries.
See exactly which domains drove the regression. Legal, medical, financial, reasoning, code — with per-domain deltas so you know where to focus manual review resources.
GitHub Actions plugin + webhook support. When a new checkpoint lands in your model registry, LLMGuard auto-fires an evaluation and posts pass/fail back to the pipeline.
- uses: llmguard/action@v1 with: threshold: 0.05 action: fail-pipeline
Add logprobs=True to your API call during testing runs. Your production pipeline stays untouched.
The LLMGuard SDK computes 5 temporal signals locally from the logprob array. Only a numerical matrix leaves your environment — never text, never raw logprobs.
IBM TTM analyzes the temporal pattern across the token sequence and returns a risk score, output class, and a structured regression report in under 20ms.
LLMGuard classifies every model response into one of four categories — giving your team a clear, actionable signal.
The model's response is reliable with high internal consistency. Safe to use.
The model sounds certain but is fabricating. The most dangerous and hardest to catch — LLMGuard's specialty.
The model is guessing and getting it wrong. Detectable from erratic response patterns.
The model doesn't know and is honestly uncertain. Not a hallucination — appropriate to flag for human review.
No other tool combines temporal signal analysis + confident hallucination detection + zero pipeline change + sub-20ms latency.
| Tool | Category | Latency | Cost / 1K evals | Pipeline Change | Confident Hallucinations | Checkpoint Regression |
|---|---|---|---|---|---|---|
LLMGuard |
Eval Pipeline | <20ms | ~$0.05 | None | ✓ Detected | ✓ Native |
| LLM-as-Judge | Pattern | 2–10s | $10–$50 | Minor | ✗ Missed | Manual setup |
| LangSmith | Observability | N/A | Medium | Production focus | ✗ No | ✗ No |
| TruLens | LLM Eval | 500ms+ | Medium | Moderate | ✗ No | Partial |
| Ragas | RAG Eval | 500ms–3s | Medium | Major (RAG only) | ✗ No | ✗ No |
| Human Red-Teaming | Manual | Days | $5k–25k | N/A | Sometimes | Expensive |
| Patronus AI | Testing | 1s+ | Medium–High | Integration | ✗ No | Partial |
"We ship checkpoints every two weeks. LLMGuard cut our hallucination verification cycle from 3 days to 12 minutes. The confident hallucination detection is the part that was genuinely missing — it caught a legal citation fabrication that had passed all our other checks."
"Our compliance team required documented evidence of automated hallucination testing before they'd reduce the QA cycle. LLMGuard gave us the audit trail, the precision metrics, and the confidence intervals. We went from 3 weeks to 4 days."
"I maintain a medical QA model that 2,000 people use monthly. Before LLMGuard I was just hoping each release wasn't regressing. Now I run a regression test before every release. It's the first time I've felt genuinely responsible about what I'm shipping."
One human red-teaming cycle costs $5,000–$25,000. LLMGuard replaces it with a 12-minute automated report. The ROI is immediate.
For small labs and individual researchers running regular checkpoint evaluations.
Overage: $0.04 / run above 10K
For ML teams shipping checkpoints regularly with real compliance requirements.
Overage: $0.012 / run above 100K
For regulated industries requiring on-premises deployment and compliance documentation.
On-Prem Air-Gapped: from $50,000/yr
14-day free trial. No credit card. Integration in under 10 minutes. First regression report in under 15 minutes.
Trusted by ML teams at AI labs, financial services firms, and healthcare organizations.