Prompt Management
The practice of versioning, testing, and managing prompts used in LLM applications. It treats prompts as code that needs proper lifecycle management.
Why It Matters
Prompt management prevents the chaos of untracked prompt changes breaking production systems. It brings software engineering rigor to LLM application development.
Example
Using a prompt management platform to track that version 3.2 of the customer support prompt improved resolution rate by 12% compared to version 3.1.
Think of it like...
Like source control for code (Git) but for prompts — every change is tracked, tested, and can be rolled back if it causes problems.
Related Terms
Prompt Engineering
The practice of designing and optimizing input prompts to get the best possible output from AI models. It involves crafting instructions, providing examples, and structuring queries to guide the model toward desired responses.
Prompt Template
A pre-defined structure for formatting prompts to AI models, with placeholders for dynamic content. Templates ensure consistent, optimized prompt formatting across applications.
MLOps
Machine Learning Operations — the set of practices that combine ML, DevOps, and data engineering to deploy and maintain ML models in production reliably and efficiently.
Evaluation
The systematic process of measuring an AI model's performance, safety, and reliability using various metrics, benchmarks, and testing methodologies.