A guide on implementing prompt engineering patterns to make RAG implementations more effective and efficient, covering patterns like Direct Retrieval, Chain of Thought, Context Enrichment, Instruction-Tuning, and more.
The article explains six essential strategies for customizing Large Language Models (LLMs) to better meet specific business needs or domain requirements. These strategies include Prompt Engineering, Decoding and Sampling Strategy, Retrieval Augmented Generation (RAG), Agent, Fine-Tuning, and Reinforcement Learning from Human Feedback (RLHF). Each strategy is described with its benefits, limitations, and implementation approaches to align LLMs with specific objectives.
Learn about how to prompt Command R: Understand the structured prompts used for RAG, formatting chat history and tool outputs, and changing sections of the prompt for different tasks.
simple example of how to use retrievers and LLMs for question answering with sources(opens in a new tab)
