by Shunku
RAG: Retrieval-Augmented Generation Fundamentals
Learn how Retrieval-Augmented Generation (RAG) combines LLMs with external knowledge to produce accurate, up-to-date responses. Understand the architecture and best practices.
6 post found
Learn how Retrieval-Augmented Generation (RAG) combines LLMs with external knowledge to produce accurate, up-to-date responses. Understand the architecture and best practices.
Learn how to use role prompting to shape LLM behavior by assigning personas and expertise. Discover when role prompting helps and potential pitfalls to avoid.
Learn how Chain-of-Thought prompting dramatically improves LLM reasoning by encouraging step-by-step thinking. Master techniques for complex problem-solving.
Learn how to use few-shot prompting to guide LLM behavior through examples. Understand zero-shot, one-shot, and few-shot techniques with practical applications.
Master the fundamentals of prompt engineering with five essential principles: Give Direction, Specify Format, Provide Examples, Evaluate Quality, and Divide Labor.
Master prompting techniques for each Bedrock model. Learn Claude's XML tags, Titan's format, Llama's special tokens, and Mistral's instruction format.