AI Quality Engineering Articles | LLM Evaluation and QA Automation

Starting My New Role as a Software Quality Assurance Manager at Wolters Kluwer

A personal update on stepping into my new leadership role, plus the quality strategy and automation priorities I am focused on first.

Response evaluation plays a critical role in ensuring AI applications are delivering accurate, meaningful, and reliable outputs.

Explore LangSmith's powerful tools for testing, debugging, and evaluating LLM applications.

Learn how improving your prompts with domain objects, element names, and clear interactions can enhance AI-driven Cypress workflows.

Essential best practices for building maintainable and reliable automation testing frameworks using Cypress and Python.

Exploring the unique challenges of testing AI applications and strategies for ensuring quality in machine learning systems.

Best practices for integrating automated testing into CI/CD pipelines to ensure quality at every stage of development.

Comprehensive strategies for testing RESTful APIs, including contract testing, performance validation, and security considerations.

Lessons learned from developing LangChain applications, including best practices for chains, agents, and memory management.

Advanced Cypress techniques for handling complex scenarios, custom commands, and performance optimization.

Best practices for testing cloud-based applications, including infrastructure testing, scalability validation, and cost optimization.

Strategies for managing test data effectively, including data generation, isolation, and cleanup techniques.

Insights on building a successful career in software quality engineering, from associate to senior level.