Common mistakes that undermine AI quality, privacy, and reproducibility
Modern AI teams have powerful tools but often rely on improvised workflows that undermine quality, privacy, and reproducibility. This white paper identifies seven common mistakes:
Each sin erodes the reliability and security of your AI workflows
Exposing sensitive information by using public AI services when private alternatives exist, risking data breaches and compliance violations.
Running AI queries one at a time instead of leveraging batch processing, wasting time and losing the ability to scale operations efficiently.
Failing to store queries and results in a central database, making it impossible to audit, search, or learn from past AI interactions.
Ad-hoc interactive sessions produce throwaway work that cannot be systematically reused, shared, or improved upon by the team.
Cramming everything into a single monolithic query instead of breaking work into focused, testable, and reusable components.
Relying on a single AI model when different models have different strengths — limiting quality, accuracy, and cost optimization.
Operating without comprehensive logging of AI interactions, making debugging, compliance auditing, and performance tracking impossible.
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