Prompt Management 101 - Full Guide for AI Engineers

Want to get started with freelancing? Let me help: https://www.datalumina.com/data-freelancer Need help with a project? Work with me: https://www.datalumina.com/solutions Effective prompt management combines structured organization, flexible templating, and systematic evaluation to create adaptable, efficient, and powerful LLM applications that evolve with your project's needs. ⏱️ Timestamps 00:00 Introduction 03:29 Common mistakes 07:44 Methods 17:51 Our approach 20:30 Example using Jinja 27:35 Best practices Jinja is a powerful Python-based template engine ideal for managing prompts, offering flexibility and versatility. We created a simple prompt manager class acting as the interface to render prompts from the template folder. We can apply conditional logic for more granular control over prompts while also validating the input variables. Conditional logic in prompts enables nuanced, context-aware instructions and responses, mirroring natural conversation adaptability while reducing token usage and eliminating the need for multiple similar prompts — something I think we are going to see more often. 👋🏻 About Me Hi! I'm Dave, a Data Scientist turned AI Engineer and founder of Datalumina®. On this channel, I share practical tutorials to help you become better at building data + AI applications. If you want to know how I help data professionals beyond these videos, then check out the links on my channel. #datalumina #prompts #genai