TLM Playground vs ZenPrompts

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.

ZenPrompts is a cutting-edge platform designed to empower prompt engineers in the creation, refinement, testing, and sharing of sophisticated prompts for various OpenAI models. This user-friendly platform features a powerful prompt editor that makes it simple to compare prompts across multiple AI models, ensuring the selection of the most suitable one based on quality, cost, and performance. The service, which is free to use during its beta release, provides valuable tools for users to develop and showcase their prompt portfolio with a minimalist design that emphasizes their creativity.

With ZenPrompts, users can experiment with prompts without the concern of losing previous work, share their inventions with a broader community, and adopt the DRY (Don't Repeat Yourself) method for efficient prompt creation using dynamic variables. Additionally, users have the option to document prompts with comments for better organization and preservation of developmental insights. The platform's goal is to enhance the capabilities of prompt engineers and to display their skills in the era of Large Language Models and AI.