Three signature pillars
Use AI to improve quality work
Requirements analysis, test generation, human review, failure analysis, and QA acceleration.
Make intelligent systems trustworthy
AI system testing, RAG validation, agents, evaluation, groundedness, safety, and regression.
Make business data trustworthy
Data quality, dimensions, pipelines, transformations, reconciliation, analytics, and operational controls.
12 cornerstone guides
These first 12 guides establish the core AskAQA model for AI and data quality. Start at the beginning or jump directly to the problem you need to solve.
What Is AI-Assisted QA?
Learn how generative AI and AI agents can augment Quality Assurance work while preserving human review, business context, traceability, privacy, and accountability.
Best Use Cases for AI in QA
A practical guide to where AI adds the most value in QA, including requirements analysis, test design, failure triage, documentation, test data, and automation assistance.
AI Test Case Generation
Learn how to generate useful test cases with AI using requirements, context, constraints, structured prompts, human review, and traceable approval.
How to Review AI-Generated Test Cases
A practical review checklist for AI-generated test cases covering requirement alignment, business correctness, coverage, duplication, expected results, test data, risk, and automation suitability.
How to Test AI Systems
Learn how testing AI systems differs from deterministic software testing and how to evaluate quality using dimensions, thresholds, datasets, human judgment, and regression baselines.
Testing RAG Applications
A practical guide to testing Retrieval-Augmented Generation systems across retrieval relevance, ranking, context quality, groundedness, citations, answer relevance, and failure behaviour.
Testing AI Agents
Learn how to test AI agents that plan, call tools, use memory, execute multi-step workflows, and perform external actions.
AI Evaluation Fundamentals
Understand AI evaluation using datasets, rubrics, reference answers, human reviewers, model graders, automated metrics, thresholds, and regression baselines.
What Is Data Quality?
A practical introduction to data quality and what it means for data to be accurate, complete, consistent, valid, timely, unique, and fit for business use.
Data Quality Dimensions
Learn the core dimensions of data quality—accuracy, completeness, consistency, validity, uniqueness, timeliness, and integrity—with practical examples.
How to Test a Data Pipeline
A six-checkpoint framework for testing data pipelines from requirements and source ingestion through transformation, curated data, business consumption, and operational readiness.
Data Reconciliation Testing
Learn how to reconcile source and target data using row counts, keys, totals, aggregates, transformations, missing records, duplicates, and exception analysis.