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Use cases

Overview​

This page provides an overview of the various example use cases for H2O Eval Studio, a modular and extensible studio designed for the evaluation of Retrieval-Augmented Generation (RAG) and Large Language Model (LLM). It highlights the benefits of using H2O Eval Studio in both development and operational phases of RAG/LLM applications. H2O Eval Studio is beneficial for audiences such as RAG/LLM developers, application developers, QA engineers, and business end-users.

Use case 1: Evaluation​

  • Overview: H2O Eval Studio can be used by developers, QA engineers, application end users, or regulators to evaluate the performance, reliability, security, fairness, and effectiveness of RAGs/LLMs in various applications.
  • Example: A user wants to assess the performance of an LLM for generating natural language descriptions of code. The user uses H2O Eval Studio to evaluate how accurately the LLM conveys the functionality of code snippets. This evaluation aids in refining the model for more precise and contextually relevant code descriptions.
  • Benefits:
    • Objectively evaluate RAGs/LLMs: Gain a comprehensive understanding of the strengths and weaknesses of RAGs/LLMs by applying a range of evaluators that assess different aspects of their performance.
    • Ensure consistent and reliable outcomes: Utilize a standardized set of evaluators to promote consistent and reliable assessments across various RAGs/LLMs and application contexts.
    • Improve RAG/LLM decision-making: Make informed decisions about the deployment and the use of RAGs/LLMs by evaluating their performance against specific criteria and in different applications.

Use case 2: Visualization​

  • Overview: H2O Eval Studio provides a powerful visualization tool that can be utilized by developers, QA engineers, or end users. Users can visualize evaluator result metrics using radar plots or bar charts to gain a clear and concise understanding of the outcomes of evaluations. H2O Eval Studio facilitates comparisons between different LLMs.
  • Benefits:
    • Enhanced comprehension: Effectively visualize and interpret multiple evaluation metrics simultaneously, enabling a comprehensive understanding of Large Language Model (LLM) performance across various dimensions.
    • Simplified comparison: Compare LLMs side-by-side for specific metrics, allowing users to identify the strengths and weaknesses of different models effectively.
    • Rapid analysis: Quickly grasp the overall performance of an LLM by examining its visual representation, thereby saving time and improving efficiency.
    • Visual insights: Gain deeper insights into the performance of LLMs by analyzing patterns and trends within plots or charts, providing valuable information for decision-making.

Use case 3: Reporting​

  • Overview: In H2O Eval Studio, users can download an HTML evaluation report to comprehensively review and analyze the results of the evaluation. The report includes an overview of tested RAG and LLMs, test lab details, executed evaluators, detailed results, error drill-down analysis, and execution logs.
  • Benefits:
    • Centralized documentation: Consolidate evaluation findings and insights into a readily accessible HTML report for organized review and reference.
    • Enhanced comprehension: Gain a deeper understanding of evaluation results by examining detailed metrics, comparisons, error breakdowns, and execution logs in a structured and comprehensive report format.
    • Shareable information: Easily share evaluation results with colleagues or stakeholders through the downloadable HTML report, promoting transparent communication and collaboration.
    • Error Root Cause Analysis: Facilitate in-depth analysis of errors and failures by providing detailed drill-down information, enabling the identification of root causes and corrective actions.

Use case 4: Problems and insights​

  • Overview: H2O Eval Studio can be utilized by developers, QA engineers, end users, or regulators to access valuable insights into the evaluation results. This includes identifying models that fail in privacy, fairness, or security; pinpointing the most problematic prompts that no compared system could answer; and detecting prompts with empty contexts.
  • Example: A user wants to evaluate an LLM's ability to detect and prevent information leakage of sensitive personal and security-related data. Using H2O Eval Studio, the user runs PII and Sensitive Data evaluators with test suites containing prompts designed to extract such information from the LLM. This evaluation helps identify potential vulnerabilities and ensures the model's reliability in handling sensitive data.
  • Benefits:
    • Rapid problem identification: Quickly identify critical issues related to privacy, fairness, security, and performance, allowing for timely corrective actions.
    • Prompt analysis: Understand the limitations of LLM/RAG models by analyzing the prompts that lead to failure or errors.
    • Evaluation quality assessment: Evaluate the overall quality and comprehensiveness of the evaluation process by identifying potential biases or shortcomings.
    • Decision-making support: Make informed decisions about the deployment, usage, and improvement of LLM/RAG models based on the insights gained from the evaluation.

Use case 5: Import​

  • Overview: H2O Eval Studio enables developers, QA engineers, or end-users to import evaluation data (including prompts, constraints, corpus, and expectations) represented as files into Eval Studio. This streamlines the creation, generation, transformation, and augmentation of test suites outside of Eval Studio and facilitates seamless integration into the evaluation process.
  • Benefits:
    • Effortless test suite integration: Minimize the manual effort involved in creating and importing evaluation data by utilizing the files in a standardized format.
    • Automated suite generation: Automate the creation, generation, transformation, and augmentation of test suites using external tools or scripts, enabling scalability and efficiency.
    • Streamlined evaluation workflow: Facilitate seamless integration of external test suites into Eval Studio's evaluation process, simplifying the overall evaluation workflow.
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