Last updated 09-01-2026
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TLM Playground
TLM Playground is Cleanlab's documentation hub for the Trustworthy Language Model (TLM), a model generation API that scores how reliable any LLM response is in real time. Each answer gets a trustworthiness score between 0 and 1, flagging hallucinations and reasoning errors before they reach users. Install the Python client with pip install cleanlab-tlm, set a CLEANLAB_TLM_API_KEY, and call TLM.prompt() to generate scored responses or get_trustworthiness_score() to audit outputs from your existing stack.
Most hallucination detectors focus on faithfulness to retrieved context. Metrics like RAGAS check whether an answer matches source documents but miss factual errors when the context is thin or confusing. TLM uses model uncertainty estimation rather than LLM-as-judge prompting, and Cleanlab publishes benchmarks showing 3x greater precision than RAGAS in RAG workflows. It needs no labeled training data on your domain, which sidesteps the drift problem that breaks custom evaluators.
ML and AI engineers building RAG pipelines, chatbots, and agent systems use TLM to gate low-confidence outputs, route them to humans, or swap in fallback answers. The API covers structured outputs, tool calls, classification labels, and multi-turn conversations, not just plain text completions.
Every response returns a 0 to 1 trustworthiness score computed via uncertainty estimation
get_trustworthiness_score() scores outputs from any LLM without changing your inference code
TLM.prompt() returns both a response and score in one API call, defaulting to gpt-4.1-mini as the base model
Benchmarks report 27% fewer incorrect GPT-4o responses and 3x better RAG error detection than RAGAS
Quality presets from low to high, plus TLM Lite, let you trade latency and cost against scoring depth
TrustworthyRAG Evals score groundedness, abstention, and context sufficiency alongside trustworthiness
Scores responses from any LLM without custom training or labeled datasets.
Supports natural language, structured outputs, tool calls, and classification decisions.
Python API offers both generation-with-scoring and score-only workflows.
Quality presets and TLM Lite help reduce latency and cost after initial testing.
Default TLM settings prioritize broad reliability over low latency and can be expensive.
Per-token pricing details are only visible inside your Cleanlab account billing page.
Trust scores may need custom evaluation criteria tuning to match team-specific quality bars.
Is TLM Playground free to use?
Yes. TLM Playground includes free tokens when you sign up for a Cleanlab account and get an API key. After those credits are used, you continue on a pay-per-token plan. View current rates in your Cleanlab account under Usage and Billing.
How do I get started with TLM Playground?
TLM Playground starts with pip install cleanlab-tlm, setting your CLEANLAB_TLM_API_KEY environment variable, and calling TLM().prompt() or get_trustworthiness_score(). The quickstart tutorial on help.cleanlab.ai walks through both methods step by step.
Can TLM Playground score responses from my own LLM?
Yes. TLM Playground scores outputs from any LLM using get_trustworthiness_score() with your prompt and response. You keep your existing inference code and add trust scoring on top without retraining on your data.
What LLM applications does TLM Playground support?
TLM Playground covers RAG, chatbots, agents, summarization, data extraction, structured outputs, tool calls, classification, data labeling, and LLM eval workflows. It scores any model output type, not just plain text responses.
Does TLM Playground offer private deployment?
Yes. TLM Playground supports enterprise VPC deployment through Cleanlab so all data stays inside your private infrastructure. Contact Cleanlab sales for private deployment options, volume discounts, and custom LLM integrations.
How can I reduce TLM Playground latency and cost?
TLM Playground lets you set quality_preset to base or low, pick faster base models like gpt-4.1-nano, use TLM Lite for score-only workflows, or stream responses from your LLM and score them afterward with get_trustworthiness_score().