https://coursiv.io/blog/tags/generative-ai--llmsRecent content in Generative AI & LLMs on Coursiv BlogHugo -- 0.147.0en-UShttps://coursiv.io/blog/glossary/ai-slopMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/ai-slopLow-quality AI-generated content produced for volume, not value.https://coursiv.io/blog/glossary/attention-mechanismMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/attention-mechanismThe operation that lets a model weigh which parts of the input matter most for each part of the output.https://coursiv.io/blog/glossary/context-windowMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/context-windowThe maximum number of tokens a model can consider at once — prompt, documents, and its own answer combined.https://coursiv.io/blog/glossary/deepfakeMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/deepfakeSynthetic audio or video that convincingly depicts a real person doing or saying something they did not.https://coursiv.io/blog/glossary/diffusion-modelMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/diffusion-modelThe architecture behind most AI image and video generation: start from noise, remove it step by step.https://coursiv.io/blog/glossary/distillationMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/distillationTraining a small model to imitate a large one, keeping most of the quality at a fraction of the cost.https://coursiv.io/blog/glossary/embeddingMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/embeddingA list of numbers representing meaning, where similar things end up close together.https://coursiv.io/blog/glossary/fine-tuningMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/fine-tuningContinuing training on your own examples to specialise a general model's behaviour.https://coursiv.io/blog/glossary/foundation-modelMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/foundation-modelA large model trained broadly once, then adapted to many downstream tasks instead of being built per task.https://coursiv.io/blog/glossary/frontier-modelMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/frontier-modelA model at or beyond the current state of the art in general capability — the ones regulators watch most closely.https://coursiv.io/blog/glossary/ganMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/ganTwo networks trained against each other — one generating fakes, one detecting them.https://coursiv.io/blog/glossary/generative-aiMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/generative-aiModels that produce new content — text, images, audio, video, code — rather than only classifying or scoring existing content.https://coursiv.io/blog/glossary/hallucinationMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/hallucinationFluent, confident output that is simply false — the defining failure mode of language models.https://coursiv.io/blog/glossary/knowledge-cutoffMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/knowledge-cutoffThe date after which a model's training data ends — everything later is invisible to it.https://coursiv.io/blog/glossary/large-language-modelMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/large-language-modelA model trained on huge amounts of text to predict the next token, which turns out to be enough to write, summarise, translate, and reason.https://coursiv.io/blog/glossary/loraMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/loraA cheap fine-tuning method that trains a small add-on instead of updating the whole model.https://coursiv.io/blog/glossary/mixture-of-expertsMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/mixture-of-expertsAn architecture where only a fraction of the model's parameters activate for any given token.https://coursiv.io/blog/glossary/multimodal-aiMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/multimodal-aiA model that handles more than one kind of input or output — text, images, audio, video.https://coursiv.io/blog/glossary/open-weights-modelMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/open-weights-modelA model whose trained weights you can download and run yourself — not necessarily open source in the strict sense.https://coursiv.io/blog/glossary/quantizationMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/quantizationStoring model weights at lower numeric precision to cut memory and cost, with a small quality trade-off.https://coursiv.io/blog/glossary/reasoning-modelMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/reasoning-modelA model trained to spend extra compute working through a problem before answering.https://coursiv.io/blog/glossary/retrieval-augmented-generationMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/retrieval-augmented-generationFetching relevant documents first and putting them in the prompt, so the model answers from sources instead of memory.https://coursiv.io/blog/glossary/rlhfMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/rlhfTraining a model on human preference judgements so it becomes helpful and well-behaved rather than merely fluent.https://coursiv.io/blog/glossary/speech-to-textMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/speech-to-textTurning spoken audio into written text.https://coursiv.io/blog/glossary/synthetic-dataMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/synthetic-dataTraining or test data generated by a model rather than collected from the real world.https://coursiv.io/blog/glossary/temperatureMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/temperatureThe setting that controls randomness in generation — low for consistency, high for variety.https://coursiv.io/blog/glossary/test-time-computeMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/test-time-computeSpending more computation at answer time — thinking longer — to get better results without retraining.https://coursiv.io/blog/glossary/text-to-imageMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/text-to-imageGenerating an image from a written description.https://coursiv.io/blog/glossary/text-to-speechMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/text-to-speechGenerating spoken audio from written text.https://coursiv.io/blog/glossary/text-to-videoMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/text-to-videoGenerating video clips from a written description.https://coursiv.io/blog/glossary/tokenMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/tokenThe unit a model actually reads and writes — usually a word fragment, not a word.https://coursiv.io/blog/glossary/tokenizationMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/tokenizationCutting text into the tokens a model can process — and a quiet source of odd model behaviour.https://coursiv.io/blog/glossary/top-pMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/top-pAn alternative randomness control that samples only from the smallest set of tokens covering probability p.https://coursiv.io/blog/glossary/transformerMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/transformerThe neural network architecture behind essentially every modern language model, built around attention.https://coursiv.io/blog/glossary/vector-databaseMon, 01 Jan 0001 00:00:00 +0000https://coursiv.io/blog/glossary/vector-databaseA database built to store embeddings and find the nearest ones fast.