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Testing Jake - Our Quality Assurance Framework Image created with DALL-E

Setting the Bar: How We're Testing Jake - LabVIEW's AI Assistant

At JKI, we're excited to share a behind-the-scenes look at how we're ensuring Jake delivers reliable, high-quality LabVIEW development assistance. We've developed a sophisticated Quality Assurance framework that sets new standards for testing AI coding assistants.

Our Testing System

We've created an innovative tool that automates the evaluation of Jake's capabilities through conversation scripts. Here's how it works:

  • Each script contains carefully crafted questions with predefined desired and undesired responses
  • The system includes logic to adapt the conversation flow based on Jake's responses
  • An advanced Language Learning Model (LLM) evaluates Jake's responses against our criteria
  • The tool generates comprehensive reports across all test scripts
  • We can measure the impact of every configuration change on Jake's performance

What makes this system particularly powerful is its ability to evaluate nuanced aspects of Jake's responses that traditional testing methods can't capture. The automation allows us to maintain consistent quality checks throughout Jake's development cycle.

We'll continue to share more about this system as we refine it and add more tests. Here's a look at our initial testing framework:

🎯 Our Testing Approach We started by creating a comprehensive suite of evaluation scripts that test Jake's knowledge across critical LabVIEW domains:

  • Core Architecture Patterns (State Machines, Producer/Consumer)
  • Advanced Development (FPGA, Actor Framework)
  • Performance Optimization
  • Error Handling
  • Data Management
  • Hardware Integration

📊 Key Metrics We Track Then, we created an evaluation tool that measures the following aspects of Jake's performance:

  • Technical Accuracy
  • Solution Completeness
  • Code Quality
  • Response Consistency
  • Context Understanding
  • Implementation Guidance

🔍 What Makes Our Testing Unique

  1. Progressive Evaluation: Multi-step conversations that adapt based on responses
  2. Quality Benchmarking: Responses evaluated against pre-defined excellence criteria
  3. Real-world Scenarios: Tests derived from actual LabVIEW development challenges
  4. Comprehensive Coverage: From basic concepts to advanced architectural patterns

💡 Working Toward a Benchmark We're excited to be developing what we believe will become an industry benchmark for LabVIEW knowledge in AI assistants. Our testing framework:

  • Establishes clear quality standards
  • Provides quantifiable metrics
  • Ensures consistent evaluation
  • Drives continuous improvement

🚀 Looking Forward This is just the beginning. We're committed to:

  • Expanding our test coverage
  • Refining evaluation criteria
  • Sharing insights with the community
  • Setting new standards for AI assistance in technical domains

We believe great tools deserve great testing, and we're excited to be pushing the boundaries of what's possible in AI-assisted LabVIEW development.

Join the Conversation

We're looking forward to sharing more insights about our testing system as it evolves. Have questions or want to learn more? Join our community on Discord - we'd love to hear your thoughts!