TLM Playground vs PromptPerfect

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.

PromptPerfect helps you improve your AI prompts for better results with models like GPT-4, ChatGPT, Midjourney, and Stable Diffusion. It offers prompt engineering to refine your inputs, optimization to boost performance, and debugging to fix issues. You can also host your text and image prompts for free, making deployment simple without extra setup.

Note that PromptPerfect will be discontinued after September 1, 2026, following Jina AI's acquisition by Elastic. ai for AI search and embedding solutions going forward.