1import{j as e}from"./ui-radix-DHIxt5ep.js";import{r as i}from"./react-vendor-CR0XoUi-.js";import{B as d}from"./book-KZat78QL.js";import{y as g}from"./index-5MFfP2q7.js";const m=[{term:"Prompt",definition:"The input text you give to an AI model to generate a response. A good prompt includes clear instructions, context, and constraints."},{term:"Context",definition:"Background information that helps AI understand your situation, audience, and goals. More context = better outputs."},{term:"Persona/Role",definition:"Telling AI to act as a specific expert or professional (e.g., 'You are a marketing consultant'). This focuses the AI's knowledge and tone."},{term:"Few-Shot Learning",definition:"Providing 1-3 examples in your prompt to show AI exactly what you want. Also called 'example-based prompting'."},{term:"Zero-Shot",definition:"Asking AI to perform a task without any examples. Works for simple tasks but often produces generic results."},{term:"Chain of Thought (CoT)",definition:"Asking AI to explain its reasoning step-by-step. Often triggered with 'Let's think step by step' or similar phrases."},{term:"Prompt Engineering",definition:"The practice of crafting and optimizing prompts to get better outputs from AI models."},{term:"Temperature",definition:"A setting that controls randomness in AI responses. Low temperature (0.1-0.3) = consistent, focused. High temperature (0.7-1.0) = creative, varied."},{term:"Token",definition:"The basic unit AI models use to process text. Roughly 1 token = 0.75 words. Most models have token limits for input and output."},{term:"System Prompt",definition:"Initial instructions that set the AI's behavior for an entire conversation. Available in ChatGPT Custom Instructions and Claude Projects."},{term:"Hallucination",definition:"When AI generates false or made-up information that sounds plausible. Always verify important facts."},{term:"Prompt Chaining",definition:"Breaking complex tasks into multiple prompts, where each builds on the previous response. Better for multi-step processes."},{term:"Output Format",definition:"Specifying how you want the response structured (bullet points, table, JSON, etc.). Be explicit about format requirements."},{term:"Constraints",definition:"Limitations you set in your prompt like word count, tone, style, or what to avoid. Helps focus the AI's response."},{term:"Iteration",definition:"Refining AI outputs through follow-up prompts. Often more efficient than trying to get everything perfect in one prompt."},{term:"Jailbreaking",definition:"Attempting to bypass AI safety measures. Not recommended and often violates terms of service."},{term:"LLM (Large Language Model)",definition:"The type of AI model that powers ChatGPT, Claude, and similar tools. Trained on vast text data to understand and generate language."},{term:"Fine-tuning",definition:"Training an AI model on specific data to specialize it for particular tasks. Different from prompting, requires technical setup."},{term:"API",definition:"Application Programming Interface - allows developers to integrate AI models into their own applications programmatically."},{term:"Multimodal",definition:"AI models that can process multiple input types (text, images, code, etc.). GPT-4V and Claude 3 are multimodal."}];function b({embedded:l=!1}){const[r,c]=i.useState(""),[o,p]=i.useState(new Set),s=m.filter(t=>t.term.toLowerCase().includes(r.toLowerCase())||t.definition.toLowerCase().includes(r.toLowerCase())),n=t=>{const a=new Set(o);a.has(t)?a.delete(t):a.add(t),p(a)};return l?e.jsxs("div",{className:`bg-gradient-to-r from-amber-50 to-orange-50 dark:from-amber-900/20 dark:to-orange-900/20 2 rounded-xl p-6 my-8 border-2 border-amber-200 dark:border-amber-800`,children:[e.jsxs("h3",{className:"text-lg font-bold text-gray-900 dark:text-white flex items-center mb-4",children:[e.jsx(d,{className:"w-5 h-5 mr-2 text-amber-500"}),"Quick Glossary"]}),e.jsx("div",{className:"grid md:grid-cols-2 gap-3",children:m.slice(0,6).map(t=>e.jsxs("div",{className:"bg-white dark:bg-gray-800 rounded-lg p-3 cursor-pointer hover:shadow-md transition-shadow",onClick:()=>n(t.term),children:[e.jsx("h4",{className:"font-semibold text-sm text-amber-600 dar
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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.