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30 AI Buzzwords Explained for Entrepreneurs 1) Large Language Model (LLM) LLMs are like super-smart computer programs that can understand and do almost anything you ask them using regular language. Think of tools like ChatGPT or Gemini – they're all powered by LLMs. 2) Prompt A prompt is simply the instruction or question you give to an LLM. Unlike traditional software, you can ask an LLM to do the same thing in many different ways. 3) Prompt Engineering Prompt engineering is the art of crafting your prompts to get the best possible results from an LLM. Tips include being specific, providing background info, organizing your request, asking the LLM to help refine your prompt, and giving examples. 4) Few-shot Prompting This is a specific prompt engineering technique where you include examples of the task you want the LLM to perform directly in your prompt. It's great for guiding the model, especially for complex tasks. 5) Context Window The context window is the maximum amount of text an LLM can "remember" or process at one time. It's like the LLM's short-term memory limit. 6) Token Tokens are the small pieces of text that LLMs use to understand and process information. Words and characters are broken down into these tokens. 7) Inference Inference is the process of an LLM generating text. It's like your phone's autocomplete, but it keeps going, picking the next best word until it creates a full response. Each token generated costs money, as the model runs for each one. 8) Parameter Parameters are the numbers inside an LLM that determine how it creates its responses. More parameters generally mean a smarter model, but also a more expensive one to run. 9) Temperature Temperature controls how creative or random an LLM's response will be. A higher temperature means more surprising or diverse outputs, while a lower temperature makes responses more predictable. 10) Prompt Injection Prompt injection is when someone tries to trick an LLM into doing something it shouldn't, like revealing private information or generating harmful content, by giving it unusual prompts. 11) Guardrails Guardrails are safety measures you put around your LLM system to prevent bad things from happening, like prompt injection. They can be simple rules or even another LLM checking the output. 12) Hallucination Hallucination is when an LLM makes up facts or information that isn't true. While useful for creative writing, it can be a problem for factual tasks. 13) Retrieval-Augmented Generation (RAG) RAG is a powerful technique that gives LLMs specific, relevant information to answer a request. This helps prevent hallucinations and gives the LLM access to up-to-date knowledge. 14) Semantic Search Semantic search finds information based on the meaning of your query, not just keywords. It helps LLMs find the most relevant context even if the exact words aren't present. 15) Embedding An embedding is a set of numbers that represent the meaning of a piece of text. Texts with similar meanings have embeddings that are numerically close to each other. for part 2 : https://medial.app/post/6839ad07d2dcb8486136d01f