What it does
Cleanlab's Trustworthy Language Model (TLM) scores the trustworthiness of responses from any LLM, so you can tell which outputs are likely correct and which need extra scrutiny. It can score answers from your existing model, or even ones written by humans. TLM can also be used in place of your own LLM, returning an answer together with its score. The documentation says it is built on top of existing frontier models and refines their generation through trustworthiness scoring, and that it needs no training on your data, no labelling and no dataset preparation. It is offered as a real-time API with settings to control cost and latency, plus private deployment options. The docs list use cases including RAG, agents, chatbots, summarization, data extraction, structured outputs, tool calls, classification, data labeling and LLM evals, with tutorials covering guardrails, LangChain, LangGraph and the OpenAI Agents SDK.
Capabilities
- Trustworthiness scores for responses from any LLM or from humans
- Option to generate answers with TLM instead of your own model
- Real-time API with cost and latency controls
- Private deployment options
- Tutorials for RAG, agents, guardrails and data extraction
When to use it
- You need to flag likely wrong answers from an LLM application before users see them
- You build RAG, agent or data-extraction pipelines that need a confidence signal
- You want to find bad responses in LLM-labelled or existing data
