Trulens

What is Trulens?

Trulens addresses a critical need in the LLMOps ecosystem by helping developers evaluate and monitor their large language model (LLM) experiments. With its innovative “feedback functions” framework, Trulens allows for the programmatic assessment of inputs, outputs, and intermediate outcomes of LLM applications. This tool provides built-in feedback functions and also supports custom functions tailored to specific applications. Trulens seamlessly integrates with frameworks like LlamaIndex and LangChain for smoother operation.

Once an LLM application is developed, Trulens can be linked to record logs and evaluate app quality using feedback functions. Developers can visualize evaluation results over time through the Trulens dashboard, simplifying the selection of the best LLM chain version for their needs.

Feedback Functions

Feedback functions augment human evaluation by processing LLM-generated text with metadata to produce a score. They can range from simple rule-based systems to more complex models used for tasks like sentiment analysis. These functions can be integrated into the application’s inferencing capabilities for large-scale monitoring, allowing developers to track LLM performance across different versions.

Examples of Feedback Functions

• Language Match: Verifies if the response language matches the prompt.
• Response Relevance: Assesses the relevance of the response using an OpenAI LLM to generate a score.
• Context Relevance: Checks if the context of an answer is relevant to the question.
• Groundedness: Validates if the answer is based on provided source content to detect potential hallucinations.

For a complete list of feedback functions, visit the Trulens documentation.

Considerations for Using Trulens

Cost is a practical factor when utilizing Trulens, particularly when feedback functions rely on APIs from other LLMs. To optimize costs, consider using free implementations or choosing comprehensive, low-cost feedback functions. Trulens is continually expanding its offerings to include more cost-effective options, ensuring that developers can achieve high-quality evaluation without significant expense.