Network Fundamentals Wiki

Prompt Engineering: Frequently Asked Questions

What is prompt engineering? Prompt engineering is the art and science of crafting effective inputs (prompts) for large language models (LLMs) like ChatGPT and Claude to elicit desired outputs. It involves understanding the model's capabilities, limitations, and how it interprets language to guide it towards generating accurate, relevant, and creative responses. Why is it called "engineering"? The term "engineering" emphasizes the iterative and systematic approach involved in prompt crafting. It's not simply writing a single sentence; it requires experimentation, refinement, and a deep understanding of how to interact with the model to achieve specific goals. This often involves testing multiple prompts, analyzing outputs, and adjusting the language, format, and structure until the desired results are consistently achieved. How do I write a good prompt? A good prompt is clear, concise, and specific, providing enough context for the model to understand the tas...

AI Basics series : All LLMs are FMs, but not all FMs are LLMs

All LLMs are foundation models, but not all foundation models are LLMs. Let's break it down to clarify: 1. All LLMs are foundation models: Foundation Models (FMs): These are large, pre-trained models trained on vast amounts of unlabelled data at scale. They are designed to be "foundational" because they can be adapted (fine-tuned) for a wide range of downstream tasks without needing to be trained from scratch for each specific application. Their pre-training allows them to learn general representations and patterns across the data. Large Language Models (LLMs): These are a specific type of foundation model that is specialized in processing and generating human language. They are trained on massive text datasets and excel at tasks like text generation, translation, summarization, and question answering. Since LLMs fit the definition of a foundation model (large, pre-trained on vast data, adaptable), every LLM is inherently a foundation model. 2. Not all foundation mode...

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