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Plain-English definitions of the AI and LLM terms that actually matter for developers building with Claude, GPT-4, Gemini, and open-source models.
RAG is a technique that feeds relevant documents to an LLM at query time, so the model generates answers grounded in your actual data instead of relying on training knowledge alone.
Open →chain of thought promptingChain-of-thought prompting is a technique where you instruct an LLM to break down its reasoning into explicit steps before producing a final answer, dramatically improving accuracy on complex problems.
Open →what is mcpMCP is an open protocol created by Anthropic that standardizes how AI models connect to external tools, databases, and APIs — giving models access to real-world capabilities through a universal interface.
Open →ai hallucinationAn AI hallucination occurs when a language model generates text that sounds confident and plausible but is factually incorrect, fabricated, or unsupported by its training data.
Open →what is fine tuningFine-tuning is the process of continuing to train a pre-trained language model on a smaller, domain-specific dataset to specialize its behavior for particular tasks or knowledge domains.
Open →prompt engineeringPrompt engineering is the practice of crafting inputs (prompts) that guide AI language models to produce accurate, relevant, and useful outputs for specific tasks.
Open →context windowThe context window is the maximum number of tokens (words and word fragments) that a language model can read and generate in a single conversation turn.
Open →embeddingsEmbeddings are dense numerical vectors that represent text (or images, audio) in a high-dimensional space where semantically similar items are positioned close together.
Open →few shot learningFew-shot learning is a prompting technique where you provide a small number of input-output examples in the prompt to teach the model the desired behavior without any fine-tuning.
Open →system promptA system prompt is a set of instructions given to an AI model before the user conversation begins, defining the model's role, personality, constraints, and behavior rules.
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