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Tools for AI Testing

There’s no one-size-fits-all tool for AI testing, but here are some tools which will help you to understand things better:

  • Explainability Tools: Tools like LIME and SHAP are essential for understanding AI model decisions. LIME is a tool designed to interpret the predictions of any machine learning model, regardless of its complexity or type. It works by creating locally interpretable approximations for a specific instance, enabling users to understand the "why" behind individual predictions.While,  SHAP values are a way to explain the output of any machine learning model. It uses a game theoretic approach that measures each player's contribution to the final outcome. In machine learning, each feature is assigned an important value representing its contribution to the model's output. SHAP values show how each feature affects each final prediction, the significance of each feature compared to others, and the model's reliance on the interaction between features.

  • LangSmith: A full-fledged platform to test, debug, and evaluate Large Language Models (LLMs) applications. It automatically logs every interaction with the LLM, allowing testers to track the conversation’s flow and context. This is crucial when trying to understand how the model handles complex queries and whether it responds consistently.

LangSmith: A Practical Example​

Imagine you're testing a healthcare assistant chatbot. You enter a query like: "Can you help me schedule a doctor’s appointment for next week?"

Evaluation Steps Using LangSmith​

1. Output Tracking
LangSmith logs the chatbot’s response, ensuring consistency across tests. It tracks how the assistant handles doctor availability, appointment times, and follow-up queries like confirming patient details. This helps assess if the chatbot delivers accurate, consistent responses.

2. Correctness Evaluation
LangSmith cross-references the chatbot’s output with predefined correct answers, such as available time slots and doctor availability. Any discrepancies, like incorrect times or unavailable doctors, are flagged. This ensures the chatbot provides reliable, accurate information.

3. Tool Selector and Metrics Evaluation
LangSmith ensures the correct backend systems or APIs are used to fetch appointment details, like the hospital's scheduling database. It evaluates the extracted metrics - such as doctor, date, and time - ensuring the chatbot presents valid options. This confirms that Al chatbot uses appropriate logic for appointment scheduling.

4. Debugging
In case of issues, LangSmith offers debugging tools to trace where the model went wrong, such as identifying if incorrect data was retrieved, or if an API failure caused an error in the chatbot’s response.