Your AI database workflow needs evidence, not just answers

📰 Dev.to · Mads Hansen

Learn to build trustworthy AI database workflows by focusing on evidence-based answers, not just answers themselves

intermediate Published 10 May 2026

Action Steps

Build a data lineage system to track data provenance
Configure AI agents to provide evidence-based answers
Test AI workflows with synthetic data to validate results
Apply data validation techniques to ensure data quality
Compare AI-generated answers with human-validated answers to measure accuracy

Who Needs to Know This

Data scientists and engineers can benefit from this approach to ensure transparency and reliability in their AI workflows, while product managers can use it to inform product decisions

Key Insight

💡 Evidence-based answers are crucial for building trustworthy AI database workflows