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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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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.→
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