Xorbix Technologies
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Company Description

Xorbix Technologies is an artificial intelligence software development company specializing in providing AI development services and machine learning development services. Our expertise includes developing advanced tools and platforms that help organizations effectively harness the power of AI, ML, and data analytics.

We created the LLM Testing Solution Accelerator, a purpose-built framework for systematically testing language models (LLMs) such as GPT-4. This accelerator ensures reliability, safety, and ethical behaviour, providing customizable testing scenarios and robust reporting capabilities, all seamlessly integrated into the Databricks platform.

Challenge

Problem

1. Traditional software testing frameworks were not designed for the unique behaviours of large language models (LLMs).

2. LLMs can generate biased, harmful, or unsafe outputs, creating significant risks in production environments.

3. Models often show inconsistencies and lack coherence across contexts, making it hard to validate reliability.

4. The black-box nature of LLMs complicates interpretability and hinders issue diagnosis.

5. Existing testing approaches fail to provide comprehensive coverage across varied use cases.

6. Lack of specialized tools to evaluate ethical and performance dimensions simultaneously.

Project Goals

Comprehensive Testing Framework

  • Build a scalable testing framework specifically tailored for LLMs.
  • Ensure coverage across diverse use cases and real-world scenarios.
  • Support modular and extensible architecture for enterprise adoption.


Customization, Interpretability & Explainability

  • Enable customizable testing scenarios and evaluation metrics.
  • Integrate domain-specific requirements for specialized industries.
  • Implement interpretability techniques to better understand model decision-making processes.


Ethical AI & Reporting

  • Incorporate responsible AI principles for bias and harmful output detection.
  • Provide robust reporting and analytics to highlight performance insights.
  • Enable data-driven decision-making through structured reporting and visualizations.

Solution

Xorbix designed and delivered the LLM Testing Solution Accelerator, a Databricks-native toolset enabling developers to test, validate, and fine-tune large language models across varied use cases.

Testing Framework Integration

  • Leveraged DeepEval as the base library for LLM testing.
  • Integrated APIs to connect different LLMs to Databricks architecture seamlessly.
  • Delivered modular architecture supporting custom evaluation metrics.

Customization & Optimization for Databricks

  • Enhanced DeepEval functions to better align with Databricks architecture
  • Optimized performance for Azure Databricks to ensure scalability.
  • Reduced token usage through metric adjustments and prompt size refinements.

Document Ingestion & Prompt-Based Testing

  • Allowed ingestion of proprietary documents into the testing pipeline.
  • Enabled generation of synthetic test cases from custom prompts.
  • Expanded validation possibilities for domain-specific use cases.

Visualization & Reporting

  • Structured results in Pandas DataFrames for advanced analytics.
  • Provided visualization options to evaluate performance trends.
  • Empowered developers to fine-tune models with detailed reporting and insights.

High Level Architecture

Innovations

  • LLM-Specific Testing: Purpose-built for unique behaviours of advanced models.
  • Databricks-Native Accelerator: Delivered as notebooks for seamless integration.
  • Custom Document Ingestion: Expanded testing beyond general benchmarks.
  • Performance Optimization: Reduced memory usage and token costs.
  • Ethical AI Alignment: Embedded safeguards to test for bias and harmful outputs.

Security

  • Role-based access to ensure only authorized developers can run or view tests.
  • Integrated ethical AI checks to mitigate risks of harmful/bias outputs.
  • Configured secure memory management for large test cases on Spark.
  • Privacy-first design with support for proprietary datasets.

Core Technologies

Frontend/Frameworks: Databricks Notebooks, DeepEval integration
Backend/Platform: Databricks Runtime, Azure Databricks
Database/Storage: Pandas DataFrames, Databricks-managed storage
Security & Ethics: Role-based access, responsible AI evaluation, bias testing mechanisms

Process

Team Composition

    • Project Manager – Coordinated accelerator design and delivery.
    • AI/ML Engineers – Developed and integrated DeepEval with Databricks.
    • Data Engineers – Managed ingestion pipelines and performance optimization.
    • QA Engineers – Validated test accuracy and ensured consistency.

Development Methodology

    • Agile approach with iterative testing and optimization cycles.
    • Early integration with client datasets for real-world validation.
    • Continuous refinement of metrics to handle diverse model behaviours.

Development Challenges

    • Memory management for large-scale test cases in Spark.
    • Token usage inefficiencies, later optimized through prompt refinements.
    • Inconsistencies across models (Llama 2, ChatGPT), addressed with DBRX integration.

Results

  • Delivered LLM Testing Solution Accelerator as a Databricks-native framework.
  • Enabled rapid prototyping and evaluation of LLMs.
  • Supported fine-tuning of pre-trained models using proprietary data.
  • Provided interactive notebooks for Q&A, indexing, and test execution.
  • Validated DBRX’s strong reasoning capabilities compared to other LLMs.
  • Positioned as a proof-of-concept solution empowering organizations to adopt responsible AI testing strategies.
Contact Xorbix

Learn how our AI software development services and Databricks solutions can help you achieve robust AI performance, compliance, and innovation.

Transform your AI capabilities with our comprehensive testing framework and trusted expertise in AI and ML development services.

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Address

802 N. Pinyon Ct,
Hartland, WI 53029

Billing Inquiries


(866) 568-8615

Information and Sales


info@xorbix.com

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