Cleanlab
ProductDetect and remediate hallucinations in any LLM application.
Capabilities8 decomposed
llm hallucination detection via confidence scoring
Medium confidenceAnalyzes LLM-generated text by computing token-level confidence scores that identify when the model is uncertain or generating unsupported content. Uses a proprietary scoring mechanism that runs inference through the LLM to extract confidence signals, enabling detection of hallucinations without requiring ground truth labels or external knowledge bases. The system flags low-confidence regions where the model is likely fabricating or confabulating information.
Uses a proprietary Trustworthy Language Model (TLM) that wraps inference calls to extract fine-grained confidence signals at the token level, rather than post-hoc fact-checking or external knowledge base matching. This approach works across any LLM and domain without requiring labeled training data.
Detects hallucinations in real-time during inference rather than requiring external fact-checking APIs or RAG systems, making it faster and more applicable to creative or domain-specific outputs where ground truth is unavailable.
automated hallucination remediation with suggested corrections
Medium confidenceWhen hallucinations are detected, the system generates corrected versions of the output by either re-prompting the LLM with confidence feedback, retrieving relevant context from a knowledge base, or synthesizing corrections from high-confidence model outputs. The remediation pipeline integrates with RAG systems and can leverage external data sources to ground responses in factual information.
Combines confidence-aware detection with generative correction by feeding confidence signals back into the LLM as structured feedback, enabling targeted re-generation of only the problematic spans rather than regenerating entire outputs.
More efficient than naive regeneration approaches because it focuses correction efforts on low-confidence regions, reducing computational overhead and latency compared to full-output retry strategies.
multi-llm hallucination comparison and consensus scoring
Medium confidenceRoutes the same prompt to multiple LLM providers (OpenAI, Anthropic, etc.) and compares their outputs to identify hallucinations through consensus mechanisms. When multiple models agree on a fact, confidence increases; when they diverge, the system flags potential hallucinations and uses agreement patterns to identify the most reliable response. This approach leverages model diversity to detect confabulations that individual models might miss.
Implements cross-model consensus as a hallucination detection signal, treating agreement patterns across diverse architectures (transformer-based, different training data) as a proxy for factuality. This is distinct from single-model confidence scoring and leverages architectural diversity.
More robust than single-model confidence scoring because it detects systematic hallucinations that fool individual models, at the cost of increased latency and expense.
confidence-aware prompt optimization and routing
Medium confidenceAnalyzes confidence scores across different prompt formulations and automatically selects or rewrites prompts that elicit higher-confidence outputs from the LLM. The system can A/B test prompt variations, identify which phrasing reduces hallucinations, and route queries to the most suitable LLM based on historical confidence patterns. This creates a feedback loop that improves prompt quality over time.
Uses confidence scores as a feedback signal to optimize prompts in a closed loop, rather than treating prompts as static. This enables data-driven prompt engineering where variations are tested and ranked by their impact on model confidence.
More systematic than manual prompt engineering because it quantifies the impact of prompt changes on hallucination rates, enabling objective comparison of alternatives.
real-time hallucination monitoring and alerting
Medium confidenceContinuously monitors LLM outputs in production, tracks confidence score distributions over time, and triggers alerts when hallucination rates exceed configurable thresholds. The system maintains dashboards showing confidence trends, identifies emerging failure modes, and can automatically throttle or disable problematic LLM endpoints. This enables proactive detection of model degradation or prompt drift.
Treats confidence scores as a first-class observability metric for LLM systems, enabling monitoring of hallucination rates the same way traditional systems monitor latency or error rates. This creates a unified quality signal across the entire LLM pipeline.
More proactive than reactive fact-checking because it detects quality degradation in real-time before users encounter hallucinations, enabling faster incident response.
confidence-based output ranking and filtering
Medium confidenceRanks multiple LLM outputs by their confidence scores and filters out low-confidence responses before delivery to users. When an LLM generates multiple candidate outputs (via beam search, sampling, or ensemble methods), the system scores each and selects the highest-confidence variant. This can also implement hard filters that reject outputs below a confidence threshold, returning a fallback response instead.
Uses confidence scores as a ranking signal for multi-candidate selection, enabling deterministic output selection based on model uncertainty rather than arbitrary heuristics or user preferences.
More principled than random selection or length-based ranking because it explicitly optimizes for reliability, making it suitable for high-stakes applications.
domain-specific hallucination detection with custom knowledge bases
Medium confidenceIntegrates with custom knowledge bases, vector stores, or domain-specific databases to ground hallucination detection in specialized knowledge. The system can retrieve relevant facts from a knowledge base and compare them against LLM outputs to identify factual inconsistencies. This enables hallucination detection in niche domains (legal, medical, scientific) where general-purpose fact-checking fails.
Combines confidence scoring with knowledge base retrieval to create a hybrid hallucination detection system that works in specialized domains where general-purpose fact-checking is insufficient. This enables detection of domain-specific confabulations.
More accurate than generic hallucination detection in specialized domains because it leverages domain-specific knowledge, but requires more setup and maintenance than general-purpose approaches.
hallucination impact assessment and risk scoring
Medium confidenceEvaluates the potential impact and risk of detected hallucinations based on context, user intent, and application domain. The system assigns risk scores that reflect the severity of hallucinations (e.g., a hallucination in medical advice is higher-risk than in creative writing). This enables prioritization of remediation efforts and helps teams decide whether to block, correct, or allow hallucinated outputs based on risk tolerance.
Moves beyond binary hallucination detection to context-aware risk assessment, enabling nuanced decisions about whether hallucinations require intervention. This reflects the reality that not all hallucinations are equally harmful.
More sophisticated than simple confidence thresholds because it considers application context and potential impact, enabling better trade-offs between safety and user experience.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓Teams deploying LLM applications in high-stakes domains (legal, medical, financial)
- ✓Builders implementing quality assurance pipelines for LLM outputs
- ✓Organizations needing real-time hallucination detection without labeled datasets
- ✓Production LLM systems requiring automated quality improvement
- ✓Teams with RAG pipelines that need confidence-aware retrieval and re-ranking
- ✓Applications where hallucinations must be corrected in-flight before user delivery
- ✓Teams with budget for multi-provider LLM calls
- ✓High-stakes applications where hallucination false positives are costly
Known Limitations
- ⚠Confidence scoring accuracy varies by model architecture and domain — no universal threshold
- ⚠Requires access to model internals or API that exposes confidence/logit information
- ⚠Does not distinguish between different types of hallucinations (factual, logical, semantic)
- ⚠Performance degrades on out-of-distribution domains where model confidence calibration breaks down
- ⚠Remediation quality depends on availability of relevant external knowledge sources
- ⚠Re-prompting adds latency (typically 500ms-2s per correction attempt)
Requirements
Input / Output
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Detect and remediate hallucinations in any LLM application.
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