Prompt Engineering vs RAG for Editing Resumes

The content details a comparison between prompt engineering and Retrieval-Augmented Generation (RAG) for editing resumes with Large Language Models (LLMs). Using Azure's code-free environment, the author evaluates both methods against metrics like groundedness, relevance, and coherence. The process involves providing a system message, a target job description, and a series of specific prompts to an LLM to generate a summary, experience bullet points, and a skills section. For the RAG approach, a knowledge base of past resumes is used to provide context, contrasting with the prompt engineering method where work history is provided directly. The experiment aims to determine if RAG produces a higher quality, more tailored resume compared to using prompts alone.