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Basic Information

TruLens is a developer-focused toolkit for systematically evaluating and tracking LLM experiments and related components such as prompts, models, retrievers, and knowledge sources. It provides fine-grained, stack-agnostic instrumentation and logging that runs alongside an application to capture model behavior and application-level events. The project codifies evaluation concepts like Feedback Functions, the RAG Triad, and Honest/Harmless/Helpful evaluations so teams can define objective feedbacks and metrics. TruLens aims to surface failure modes, support iterative improvement of LLM-based systems, and present experiment comparisons through an easy-to-use user interface. The repository includes installation instructions, quickstart examples and notebooks, and links to documentation and community contribution guidance.

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App Details

Features
Instrumentation and logging primitives that integrate with LLM apps to capture prompt and model interactions. Configurable Feedback Functions for defining automated evaluation checks and metrics. Support for evaluating retrieval-augmented generation workflows via the RAG Triad concept. Stack-agnostic design so it can be applied across different model providers and application frameworks. Quickstart examples and Colab notebooks to demonstrate common workflows. A user interface for comparing versions and visualizing evaluations. Project metadata shows publishing and CI badges, documentation resources, and community/contributing pointers. Package distribution via PyPI makes installation straightforward.
Use Cases
TruLens helps developers and teams discover and diagnose model and application failure modes by running systematic, repeatable evaluations while an app is exercised. By specifying feedbacks and capturing fine-grained traces, teams can measure the impact of prompt or model changes, compare experiment versions in the UI, and prioritize fixes. The tooling supports RAG workflows so retrieval, knowledge sources, and generation can be evaluated together. Quickstart notebooks accelerate onboarding, and the pip package enables easy installation into existing projects. Overall, TruLens shortens iteration cycles for LLM apps by making behavior observable, quantifiable, and comparable across experiments.

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