In this episode, Aishwarya Reganti and Kiriti Badam introduce a powerful new framework for building AI products: the Continuous Calibration/Continuous Development (CC/CD) framework.

If you’ve ever shipped an AI demo that looked magical but struggled to scale, this framework will resonate. The CC/CD framework provides a practical and structured approach to navigating these realities and building AI systems that are stable, intentional, and trustworthy.

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In this episode, you’ll learn:

• The two core differences every AI builder must account for: non-determinism and the agency–control tradeoff

• The six phases of the CC/CD loop

• How to scope capabilities across different versions of your product to gradually earn trust and agency

• The role of reference datasets in taming unpredictability and guiding evals

• How to design application-specific evals that act as the equivalent of tests for AI

• Why control handoffs are essential for maintaining user trust

• How to transform deployment from a finish line into the start of continuous calibration

• Why you should never “jump to full agency” before the system earns it

• How to apply the CC/CD loop with real-world examples: customer support, marketing assistants, and coding copilots

References:

• Beyond vibe checks: A PM’s complete guide to evals: https://www.lennysnewsletter.com/p/beyond-vibe-checks-a-pms-complete

• “Building agentic AI applications with a problem-first approach” [Maven course]: https://maven.com/aishwarya-kiriti/genai-system-design

• Cursor: https://cursor.com

• “Don’t build AI products like traditional software” [Free lightning talk]: https://maven.com/p/88a325/don-t-build-ai-products-like-traditional-software

• GitHub Copilot: https://github.com/features/copilot

• Why premature pptimization is the root of all evil: https://stackify.com/premature-optimization-evil/

• Why your AI product needs a different development lifecycle: https://www.lennysnewsletter.com/p/48e7fb02-1fc1-4bb7-bf85-85f4165e8225

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