Source: Lenny’s Newsletter by Lenny Rachitsky. Read the original article
TL;DR Summary of Introducing the Continuous Calibration/Continuous Development Framework for AI Products
The Continuous Calibration/Continuous Development (CC/CD) framework offers a structured method for building stable, trustworthy AI systems. It addresses key AI challenges like non-determinism and balancing agency versus control. The framework outlines six phases to help developers progressively scope capabilities and earn user trust. Additionally, it emphasizes the importance of reference datasets and tailored evaluations for managing unpredictability.
Optimixed’s Overview: Mastering AI Product Stability with the Continuous Calibration/Continuous Development Approach
Understanding the CC/CD Framework for AI Development
The Continuous Calibration/Continuous Development (CC/CD) framework is designed to help AI builders overcome common hurdles in scaling AI demos into reliable products. By recognizing the inherent non-determinism of AI and the tradeoff between agency (user control) and system autonomy, this approach guides teams through a systematic process.
Key Components and Benefits
- Six Phases of the CC/CD Loop: These stages provide a roadmap for iterative development, helping teams scope product capabilities across versions to incrementally build trust.
- Managing Non-Determinism: Through continuous calibration, the framework mitigates unpredictability in AI outputs, enhancing stability.
- Reference Datasets: Serving as benchmarks, these datasets are critical for guiding evaluations and maintaining consistent performance.
- Application-Specific Evaluations: Tailored evals act as practical checks, ensuring the AI behaves as intended within its specific context.
By adopting the CC/CD framework, AI product teams can build systems that are not only innovative but also maintainable and reliable, fostering greater user trust and smoother scaling from demos to production-ready solutions.
