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Core
Prompt Versioning and Management
Prompt versioning treats prompts as production artifacts with their own change log, eval-backed releases, and rollback path, instead of string literals buried in application code. The key move is decoupling prompt changes from code deploys so a regression in output quality can be reverted in seconds without shipping a new binary. Applied AI interviews probe it because a candidate who edits prompts in place and ships on vibes will silently degrade quality in production.
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COURSES COVERING THIS TOPIC
No lesson covers this one directly yet. These teach the surrounding topic from the beginning.
Applied AI Engineering·The interviewPremium14mDriving the design conversationA design round is a conversation you are expected to lead, not a question you answer. This lesson is the shape that works, the four moments that decide the outcome, and the two classic ways strong candidates lose one.Applied AI Engineering·The interviewPremium12mTurning this course into a study planA concrete four-week plan mapping the seven modules onto the question bank, plus what to do differently if your interview is next week rather than next month.
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
MLOps & ML EngineeringCompare shadow, canary, and blue-green deployment for ML models, and how you roll back safely.→MLOps & ML EngineeringHow does LLMOps differ from traditional MLOps, and how do you version and manage prompts in production?→MLOps & ML EngineeringHow do you decide when to roll back a deployed model, and how do you do it safely?→MLOps & ML EngineeringHow do you build an evaluation harness that runs in CI to gate every model change?→MLOps & ML EngineeringDesign an evaluation pipeline for an LLM application that runs on every prompt and model change.→System Design for AI in ProductionDesign a private LLM deployment for a regulated enterprise where nothing, not even telemetry, may leave the network.→
COMPANIES THAT ASSUME THIS