Cleanlab
Detect and remediate hallucinations in any LLM application.
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What is Cleanlab?
Cleanlab helps you identify and fix hallucinations in Large Language Models before they reach users. Hallucinations occur when LLMs generate plausible-sounding but factually incorrect information, which can damage trust and cause real harm in production systems. Cleanlab's Trust Language Model (TLM) analyses LLM outputs to flag unreliable responses with high accuracy, allowing you to catch problems early.
The tool works across any LLM application, whether you're using OpenAI, Anthropic, open-source models, or your own fine-tuned versions. Rather than replacing your existing setup, Cleanlab sits alongside your LLM to provide confidence scores on generated text. You can then decide whether to ask the model to regenerate, route requests to human reviewers, or adjust your prompts. This approach makes it practical for teams who need quality control without rebuilding their entire system.
Key Features
Hallucination detection
Identifies when LLM outputs contain fabricated or unreliable information
Confidence scoring
Provides trustworthiness scores for any LLM response
Multi-model support
Works with proprietary and open-source language models
API integration
Embeds into your existing LLM pipelines and applications
Real-time analysis
Processes outputs as they're generated for immediate feedback
Pros & Cons
Advantages
- Works with any LLM, so you're not locked into a specific model provider
- Free tier lets you test the approach before committing budget
- Reduces the risk of deploying unreliable AI outputs to users
- Provides actionable confidence signals rather than just warnings
Limitations
- Adds latency to LLM responses since outputs need to be analysed before returning to users
- Requires integration work to embed into existing applications; not a plug-and-play solution
- Detection accuracy depends on the specific domain and LLM being used
Use Cases
Customer support chatbots where incorrect information could frustrate users or create liability
Medical or legal AI assistants where accuracy is critical for decision-making
Research tools that summarise documents; catching hallucinations prevents spreading false claims
Content generation platforms where fact-checking is needed before publishing
AI-assisted coding tools where incorrect suggestions could introduce bugs