Title: Quiz 14 — Prompt Management, Versioning, and Release Workflows
Course: AIINFRA 301: Production RAG & LLMOps — Observability and Evaluation > Section: Week 14 — Prompt Management, Versioning, and Release Workflows

Q: Storing your prompt as a file named prompt_v2_final.txt provides adequate version control for a production RAG application.
- True
- False
A: False

Q: What is the core rule of prompt version immutability in a prompt registry?
- Versions can be edited freely as long as you leave a comment
- Once a version is created, it is never modified — any change creates a new version
- Only the most recent version is retained; older ones are deleted
- Versions can only be edited by an administrator
A: Once a version is created, it is never modified — any change creates a new version

Q: Name the two prompt-versioning tools identified as the most widely deployed in 2026, and name the tool that was acquired and shut down.
Feedback: Sample/expected answer: Langfuse and PromptLayer are the most widely deployed; Humanloop was acquired by Anthropic and shut down (September 2025).

Q: In Langfuse, what mechanism lets you deploy a specific prompt version to production without redeploying your application?
- Rebuilding the Docker image with the new prompt baked in
- Editing the existing production version's text directly
- Assigning an environment label (e.g., `production`) to point at a specific version, which the app fetches at runtime
- Restarting the application server with a new environment variable
A: Assigning an environment label (e.g., `production`) to point at a specific version, which the app fetches at runtime

Q: A "cleaner" or shorter rewrite of a prompt is always a safe change that can be pushed directly to production without going through staging evaluation.
- True
- False
A: False

Q: Why does Langfuse's approach to prompt versioning integrate especially well with observability?
- It automatically rewrites prompts based on trace data
- It links every traced request to the exact prompt version that produced it
- It hides prompt versions from application logs for security
- It requires no evaluation before promoting to production
A: It links every traced request to the exact prompt version that produced it

Q: Describe what a "staging-gate evaluation" accomplishes in a prompt release workflow.
Feedback: Sample/expected answer: It requires a new prompt version to pass automated evaluation in staging before it can be promoted (labeled) to production, catching regressions — such as a silently dropped instruction — before they reach users.

Q: A prompt version that silently drops a critical instruction (like "always cite your sources") is a realistic risk primarily because:
- Prompt registries automatically delete instructions over 100 words
- Shorter or "cleaner" edits can look like harmless refactors while changing model behavior in ways that must be caught by evaluation, not assumed safe
- Langfuse enforces a maximum prompt length
- Labels cannot be reassigned once set
A: Shorter or "cleaner" edits can look like harmless refactors while changing model behavior in ways that must be caught by evaluation, not assumed safe