Concepts IA Fondamentaux
Artificial Intelligence (AI)
The simulation of human intelligence by computer systems, including learning, reasoning, and self-correction. In 2025, AI most commonly refers to large language models (LLMs) that can understand and generate text, code, and images.
Large Language Model (LLM)
A neural network trained on massive text datasets that can understand and generate human-like text. Examples: GPT-4, Claude, Gemini, Llama. LLMs power most modern AI applications including coding assistants, chatbots, and content generators.
Machine Learning (ML)
A subset of AI where systems learn patterns from data without being explicitly programmed. ML algorithms improve through experience. Common types: supervised learning, unsupervised learning, and reinforcement learning.
Deep Learning
A subset of machine learning using neural networks with multiple layers (deep neural networks). Deep learning powers image recognition, natural language processing, and modern AI assistants.
Natural Language Processing (NLP)
The ability of computers to understand, interpret, and generate human language. NLP enables chatbots, translation, sentiment analysis, and AI coding assistants.
Neural Network
A computing system inspired by the human brain, consisting of interconnected nodes (neurons) organized in layers. Neural networks are the foundation of modern AI, processing information through weighted connections.
Token
The smallest unit of text that an AI model processes. A token is roughly 4 characters or 0.75 words in English. Models have token limits (context windows) that determine how much text they can process at once.
Context Window
The maximum number of tokens an AI model can process in a single interaction. Claude has a 200K token context window, GPT-4 has 128K. Larger context windows allow AI to understand bigger codebases and longer documents.
Hallucination
When an AI model generates information that is plausible-sounding but factually incorrect. Hallucinations are a key challenge in AI — always verify AI outputs, especially for facts, dates, and technical details.
Temperature
A parameter that controls the randomness of AI output. Low temperature (0.0-0.3) produces more deterministic, focused responses. High temperature (0.7-1.0) produces more creative, varied outputs.
Developpement & Ingenierie
Prompt Engineering
The skill of writing effective instructions (prompts) for AI models to get optimal outputs. Good prompts include context, examples, constraints, and clear formatting requirements. A core skill for AI-assisted development.
RAG (Retrieval-Augmented Generation)
A technique that enhances AI responses by first retrieving relevant information from a knowledge base, then using it as context for generation. RAG reduces hallucinations and keeps AI responses grounded in your specific data.
Fine-Tuning
The process of further training a pre-trained AI model on your specific data to improve its performance for a particular task. Fine-tuning customizes a general model to your domain, terminology, and requirements.
Embeddings
Numerical representations (vectors) of text that capture semantic meaning. Similar texts have similar embeddings. Used for semantic search, recommendation systems, and RAG implementations.
Vector Database
A specialized database optimized for storing and searching embeddings. Examples: Pinecone, Weaviate, Chroma, Supabase pgvector. Essential for building RAG applications and semantic search.
AI Agent
An AI system that can autonomously plan, execute, and iterate on tasks. Unlike chatbots that respond to single prompts, agents can break down complex goals into steps, use tools, and self-correct. Examples: Claude Code, Devin.
Multi-Agent System
A system where multiple AI agents collaborate to complete complex tasks. Each agent has a specialized role (researcher, coder, reviewer) and they coordinate through messages. A key pattern in advanced AI development.
MCP (Model Context Protocol)
An open protocol by Anthropic that standardizes how AI tools connect to external data sources, APIs, and services. MCP servers expose tools, resources, and prompts that any MCP-compatible AI client can use.
Vibe Coding
A development approach where you describe what you want in natural language, and AI tools generate the code. Coined in 2025, vibe coding emphasizes intent and architecture over syntax and memorization.
AI Pair Programming
Using an AI coding assistant alongside your own coding, similar to traditional pair programming with a human partner. The AI suggests code, catches bugs, and handles repetitive tasks while you focus on architecture and logic.
Function Calling (Tool Use)
The ability of an AI model to call external functions or APIs during a conversation. The model decides when to call a function, formats the arguments, and processes the results. Enables AI agents and integrations.
Streaming
Delivering AI responses token-by-token in real-time instead of waiting for the complete response. Streaming creates a more responsive user experience, especially for chat interfaces and code generation.
Chain-of-Thought
A prompting technique that asks the AI to reason through a problem step by step before giving a final answer. Significantly improves accuracy on complex reasoning, math, and multi-step coding tasks.
Few-Shot Prompting
Providing 2-5 examples of the desired input/output format in your prompt before the actual request. The AI learns the pattern from examples and applies it to your task. Highly effective for consistent output formatting.
Zero-Shot Prompting
Asking an AI to perform a task without providing any examples — just the instruction. Works well for simple tasks but less reliable for complex or unusual formats.
IA Business & Bureau
AI Automation
Using AI to automate business processes that previously required human judgment. Examples: email triage, report generation, lead scoring, content creation, and customer support. Differs from traditional automation by handling unstructured data and decisions.
No-Code AI
Building AI-powered applications and automations without writing code. Tools like Zapier, Make, and Bubble let non-technical users create sophisticated AI workflows through visual interfaces.
AI Copilot
An AI assistant embedded within a software application to help users work more efficiently. Microsoft Copilot (in Office apps), GitHub Copilot (in code editors), and Salesforce Einstein are examples.
Generative AI
AI that creates new content — text, images, code, music, video — rather than just analyzing existing data. ChatGPT, Claude, DALL-E, and Midjourney are generative AI tools.
AI Literacy
The ability to understand, use, and critically evaluate AI tools and their outputs. In 2025, AI literacy is becoming as essential as computer literacy was in 2000.
Prompt Library
A curated collection of effective prompts for specific tasks. Building a personal prompt library is one of the highest-ROI activities for AI users — reusable prompts save time and ensure consistent quality.
AI ROI
The return on investment from implementing AI tools and training. Measured in time saved, productivity gains, error reduction, and revenue impact. Well-implemented AI typically delivers 5-20x ROI within the first year.
Workflow Automation
Connecting multiple apps and services to automate multi-step business processes. With AI, workflow automation can now handle tasks requiring judgment — like categorizing emails, scoring leads, or generating reports.
Outils & Plateformes IA
Cursor IDE
An AI-powered code editor (fork of VS Code) with built-in AI features: Composer for multi-file edits, Tab completion, and Agent mode for autonomous coding. The most popular AI coding IDE in 2025.
Claude Code
Anthropic's command-line AI coding agent. Operates in your terminal, reads entire codebases, writes files, runs commands, and manages git. Best for experienced developers who prefer terminal workflows.
GitHub Copilot
Microsoft/GitHub's AI pair programmer. Provides inline code suggestions, chat-based coding help, and deep GitHub integration. Available in VS Code, JetBrains IDEs, and the CLI.
Windsurf
An AI code editor by Codeium with a generous free tier. Features Cascade for multi-step coding tasks and strong autocomplete. A popular free alternative to Cursor.
LangChain
An open-source framework for building applications with large language models. Provides tools for chaining LLM calls, managing memory, using external tools, and building AI agents.
Vercel AI SDK
A TypeScript library for building AI-powered web applications. Handles streaming, chat interfaces, and AI provider integration. Works with Next.js, React, and other frameworks.
Zapier
A no-code automation platform connecting 7,000+ apps. With AI integration, Zapier can now make intelligent decisions in workflows — categorize data, generate content, and process unstructured information.
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