October 18, 2026
What is Training Data? Explained Simply
Training data is the collection of examples an AI learns from — its abilities, knowledge, and blind spots all come directly from what it was shown.
Read more →Plain-English explanations of AI terms, from LLMs to diffusion models.
Training data is the collection of examples an AI learns from — its abilities, knowledge, and blind spots all come directly from what it was shown.
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In AI, inference is when a trained model makes predictions from new input — every chatbot reply and generated image is the result of inference.
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LoRA (Low-Rank Adaptation) customizes a large AI model by training a tiny add-on — under 1% of its size — instead of retraining the whole thing.
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Quantization shrinks an AI model by storing its numbers with less precision — letting powerful models run on ordinary laptops and phones.
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A context window is the amount of text an AI can read and remember at once — its short-term memory. Learn how it works and why size matters.
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A vector database stores data by meaning using number lists called vectors — the technology behind AI search, recommendations, and chatbot knowledge.
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Chain-of-thought prompting asks AI to show its step-by-step reasoning before answering — a simple trick that makes answers to complex questions far more accurate.
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Few-shot learning: show AI a few examples and it follows the pattern. Learn how to write example prompts that get accurate, on-style results.
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Zero-shot learning means AI can do new tasks with zero examples. Learn how instruction-following works, when it shines, and when to add examples.
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Transfer learning lets new AI models reuse knowledge from old ones. Learn how fine-tuning works and why it powers most real-world AI products.
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