The provided text, a transcript from an IBM Technology YouTube video, explains the distinctions and interplay between prompt engineering and context engineering in the realm of large language models (LLMs). Prompt engineering focuses on crafting precise instructions and examples within the input text to guide an LLM's output, utilizing techniques such as role assignment, few-shot examples, chain-of-thought prompting, and constraint setting. Context engineering, a broader discipline, encompasses the programmatic assembly of all information an LLM utilizes during inference, including the prompt itself, retrieved documents, memory, and tools, to ensure accurate responses. This involves components like memory management (short-term and long-term), state management for multi-step processes, Retrieval Augmented Generation (RAG) for dynamic knowledge retrieval, and tool integration to enable LLMs to perform real-world actions. Ultimately, the text argues that while prompt engineering enhances the questions posed to an LLM, context engineering builds more effective systems by dynamically populating prompts with relevant information from memory, retrieved data, and real-time states.
