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Company Description

Xorbix Technologies specializes in providing artificial intelligence and machine learning solutions. Our expertise includes developing advanced tools and platforms that help organizations effectively harness the power of AI and data analytics. We created a robust testing solution like the LLM Testing Solution Accelerator, designed specifically for large language models such as GPT-4. This accelerator ensures reliability, safety, and ethical behavior, providing customizable testing scenarios and robust reporting capabilities, all seamlessly integrated into the Databricks platform.

Challenge

Problem

Existing testing frameworks and methodologies are primarily designed for traditional software systems and may not effectively capture the unique characteristics and potential failure modes of LLMs. These advanced models can exhibit unexpected behaviors, generate biased or harmful outputs, and struggle with consistency and coherence across different contexts. Furthermore, the opaque nature of these models complicates understanding their decision-making processes, making it challenging to diagnose and address issues. As a result, conventional testing approaches fall short in identifying and mitigating risks associated with LLMs, necessitating the development of specialized testing strategies that can evaluate their performance, reliability, and ethical implications comprehensively.

Project Goals

  • Develop a comprehensive testing framework: Create a flexible and scalable testing framework specifically tailored for LLMs, capable of evaluating their performance across a wide range of scenarios and use cases.
  • Enable customizable testing: Provide a modular and extensible architecture that allows for the integration of custom testing scenarios, evaluation metrics, and domain-specific requirements.
  • Facilitate interpretability and explainability: Implement techniques to enhance the interpretability and explainability of LLM outputs, enabling a better understanding of their decision-making processes and facilitating more effective testing.
  • Ensure ethical and responsible testing: Incorporate ethical considerations and responsible AI principles into the testing framework, ensuring that LLMs are evaluated for potential biases, harmful outputs, and alignment with societal values.
  • Provide comprehensive reporting and analytics: Develop robust reporting and analytics capabilities to provide detailed insights into LLM performance, identify areas for improvement, and support data-driven decision-making.

By addressing these goals, the LLM Testing Solution Accelerator aims to empower organizations and researchers with a powerful toolkit for rigorously evaluating and validating the safety, reliability, and ethical behavior of large language models, fostering trust and enabling responsible deployment of these advanced AI systems.

Challenge

Problem

Existing testing frameworks and methodologies are primarily designed for traditional software systems and may not effectively capture the unique characteristics and potential failure modes of LLMs. These advanced models can exhibit unexpected behaviors, generate biased or harmful outputs, and struggle with consistency and coherence across different contexts. Furthermore, the opaque nature of these models complicates understanding their decision-making processes, making it challenging to diagnose and address issues. As a result, conventional testing approaches fall short in identifying and mitigating risks associated with LLMs, necessitating the development of specialized testing strategies that can evaluate their performance, reliability, and ethical implications comprehensively.

Project Goals

  • Develop a comprehensive testing framework: Create a flexible and scalable testing framework specifically tailored for LLMs, capable of evaluating their performance across a wide range of scenarios and use cases.
  • Enable customizable testing: Provide a modular and extensible architecture that allows for the integration of custom testing scenarios, evaluation metrics, and domain-specific requirements.
  • Facilitate interpretability and explainability: Implement techniques to enhance the interpretability and explainability of LLM outputs, enabling a better understanding of their decision-making processes and facilitating more effective testing.
  • Ensure ethical and responsible testing: Incorporate ethical considerations and responsible AI principles into the testing framework, ensuring that LLMs are evaluated for potential biases, harmful outputs, and alignment with societal values.
  • Provide comprehensive reporting and analytics: Develop robust reporting and analytics capabilities to provide detailed insights into LLM performance, identify areas for improvement, and support data-driven decision-making.

By addressing these goals, the LLM Testing Solution Accelerator aims to empower organizations and researchers with a powerful toolkit for rigorously evaluating and validating the safety, reliability, and ethical behavior of large language models, fostering trust and enabling responsible deployment of these advanced AI systems.

Solution

  • Finding a testing library: In our quest to streamline the testing process for large language models (LLMs), we discovered a powerful testing library called DeepEval. This library served as a solid foundation and provided a template for running comprehensive tests on our LLMs. By leveraging DeepEval’s robust features, we were able to kickstart our testing efforts efficiently.
  • Setting up LLM APIs: To showcase the versatility of our product and cater to the diverse needs of our users, we included examples that demonstrate how developers can seamlessly connect their LLMs to our testing platform. These examples not only serve as a practical guide but also highlight the ease of integration, enabling users to evaluate and test their LLMs without any hassle.
  • Customization: While DeepEval provided a strong starting point, we recognized the need for customization to ensure optimal performance on the Databricks platform. Our team meticulously tailored specific testing functions to align with the unique requirements of the Databricks environment. This customization effort ensured a seamless and efficient testing experience for our users, leveraging the full potential of the Databricks platform.
  • Document Ingestion and custom prompts: One of the key features we introduced is the ability for developers to ingest their documents with ease. This streamlined process allows users to effortlessly incorporate their own documents into the testing pipeline, enabling them to create comprehensive test cases tailored to their specific use cases. Additionally, we added the capability to generate custom documents from prompts, further expanding the testing possibilities and ensuring a thorough evaluation of LLM performance.
  • Visualization and Results: To facilitate effective analysis and interpretation of LLM performance, we store the testing results in a pandas dataframe. This structured format not only ensures data integrity but also empowers developers to leverage the full potential of data manipulation and visualization tools. By providing a flexible and accessible data format, our users can easily explore, analyze, and visualize the testing results, gaining valuable insights into their LLM’s performance and identifying areas for improvement.

LLM Testing Solution Accelerator for Databricks

Some Sample Visualizations

LLM Testing Solution Accelerator for Databricks

LLM Testing Solution Accelerator for Databricks

System Requirements

  • Designed to run in the Databricks environment.
  • Will be sold as a Databricks notebook so the target user will be developers.
  • When running many test cases, having more spark memory is recommended.
  • Spark memory should be configured based on how many test cases the user is running.
  • If running more than 1000 test cases at once, it is recommended to increase spark memory to avoid garbage collection prompt.

Process

General Development

  • Approached development systematically, by analyzing what tools would take the most time to make. At first, we developed the document ingestion and ran it with an unmodified version of the DeepEval library.
  • Discovered there were some issues with memory, even on larger clusters to which we started modifying the DeepEval library on metrics that would commonly be used.
  • Reduced the number of tokens sent to the LLM by adjusting the prompts as well as fixed the memory issue.
  • Added visualization to better interpret results.

Testing

  • One of the challenges we faced was to find the correct LLM for evaluation.
  • First, we checked LLMs like Llama 2 and ChatGPT, but they were not returning good results.
  • Databricks release their DBRX model during development and returned the best results for an evaluation LLM.
  • Through our testing we found that users should focus on LLMs with high reasoning power.

Process

General Development

  • Approached development systematically, by analyzing what tools would take the most time to make. At first, we developed the document ingestion and ran it with an unmodified version of the DeepEval library.
  • Discovered there were some issues with memory, even on larger clusters to which we started modifying the DeepEval library on metrics that would commonly be used.
  • Reduced the number of tokens sent to the LLM by adjusting the prompts as well as fixed the memory issue.
  • Added visualization to better interpret results.

Testing

  • One of the challenges we faced was to find the correct LLM for evaluation.
  • First, we checked LLMs like Llama 2 and ChatGPT, but they were not returning good results.
  • Databricks release their DBRX model during development and returned the best results for an evaluation LLM.
  • Through our testing we found that users should focus on LLMs with high reasoning power.

Results

Achieved the product we set out to make, a solution accelerator for the Databricks platform which helps evaluate custom large language model (LLM) solutions. Developers can use this to further test and prototype LLM applications tailored to their specific use cases. The accelerator provides a framework for fine-tuning pre-trained LLMs with an organization’s proprietary data, enabling rapid iteration and evaluation of domain-specific natural language processing capabilities. It includes interactive notebooks for building a document index, assembling a Q&A application powered by the fine-tuned LLM, and testing the application’s performance. While not intended for direct production deployment, this accelerator serves as a valuable proof-of-concept and starting point for organizations to unlock the potential of LLMs and explore their integration into their data and AI workflows on the Databricks platform.

Contact Xorbix Technologies today to learn how our LLM Testing Solution Accelerator can help you achieve robust AI performance and compliance. Transform your AI capabilities with our comprehensive testing framework.

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802 N. Pinyon Ct, Hartland, WI 53029

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802 N. Pinyon Ct, Hartland, WI 53029 (866) 568 8615
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