• AI and LLM Terms from A to Z: The Complete Glossary for Beginners | llmnet.nlDiscover the most important AI and LLM terms in this complete glossary for beginners. From RAG and tokens to fine-tuning, explained simply and clearly.
• The Attention Mechanism in AI Explained Simply - llmnet.nlLearn how the attention mechanism in LLMs works. A clear explanation of context, query/key/value, self-attention, and quadratic scaling without heavy math.
• Chain-of-thought: Letting a model reason in steps | llmnet.nl LearnDiscover how to increase the reliability of AI models with Chain-of-Thought (CoT) prompting. Learn the theory, pitfalls, and practice with real-world examples.
• Context Engineering Explained: Why the Prompt is Not Enough | LLMnetDiscover what context engineering is, how to assemble dynamic context (RAG, memory, tools), and why a larger context window is not always better.
• Model Distillation Explained: How Small AI Models Learn from the Giants | llmnet.nlDiscover how model distillation works: the technique that allows small AI models to adopt the knowledge of large models for faster and cheaper performance.
• Embeddings explained: how machines understand meaning | llmnet.nl LearnDiscover how AI understands text through embeddings and vector spaces. Learn the basics of similarity, semantic search, and clustering in this compact module.
• Fine-tuning vs prompting vs RAG: when to choose what? | Leren.llmnet.nlDiscover when to choose prompting, RAG, or fine-tuning. Compare concepts, costs, complexity, and use the decision tree for your AI application.
• Memory in LLM Applications: How AI Learns to Remember and ForgetDiscover how memory works in LLM applications. Learn the difference between context windows, conversation history, summaries, and how to forget in a controlled way.
• AI Guardrails Explained: The Safety Filters of Language ModelsDiscover how AI guardrails work, the difference between training alignment and runtime filters, and why language models sometimes refuse to answer.
• Why Does AI Sometimes Make Things Up? Understanding and Mitigating Hallucinations | LLMnet LearnDiscover why language models hallucinate, how to recognize incorrect AI answers, and which techniques (such as RAG) you can use to prevent this.
• How does an AI learn? Training explained for beginners | llmnet.nlDiscover intuitively how artificial intelligence learns. Learn about data quality, pattern recognition, and the difference between training, fine-tuning, and using.
• AI Inference Explained: What Happens Under the Hood?Discover what AI inference is. Learn step-by-step how a language model processes your prompt, from tokens and the forward pass to generating a response.
• Getting Started with LLMs: From Zero to Your First Application | LLMNet LearningDiscover how Large Language Models (LLMs) work in this practical learning path. Learn effective prompting, API basics, and key privacy guidelines.
• LoRA and Adapters Explained: Cheap Fine-Tuning | llmnet.nlDiscover how LoRA and adapters make fine-tuning LLMs affordable and accessible. Learn the technique, benefits, and implementation for efficient AI.
• Mixture-of-Experts (MoE) Explained: Why Modern AI Doesn't Use All Its Parameters at OnceDiscover how Mixture-of-Experts (MoE) makes modern AI models faster and smarter by not using all parameters at once. Read how it works.
• Parameters and weights: what is in an AI model? | Learn - LLMnetDiscover what parameters and weights in an AI model are, why size matters, and the difference between training and inference with a clear analogy.
• Prompting for Non-Technical Users: Get More Out of AI Without Code | Leren.llmnet.nlDiscover how to write smart AI prompts as a non-technical user. Learn to ask clear questions, provide context, and iterate for immediate results without code.
• RAG Explained for Beginners: Making Your Own Documents Searchable with an LLMLearn the basics of Retrieval-Augmented Generation (RAG). Discover how embeddings and vector databases work and make your own data searchable with AI.
• What is RLHF? Reinforcement Learning from Human Feedback and Alignment Explained Simply | LLMnetDiscover how AI models are aligned using RLHF and DPO. Learn how human feedback ensures safe, helpful, and nuanced responses from LLMs.
• Temperature, top-p and other sampling parameters explained - Leren.llmnet.nlDiscover how temperature, top-p, and penalties affect the output of LLMs. Learn when to choose creativity or reliability with practical examples.
• Tokenization explained: how an LLM chops text into pieces | LLMnet LearningDiscover what tokenization is, how large language models process text, and why this is crucial for API costs and the context window of your AI application.
• Training Data and Bias in Language Models: Causes and SolutionsDiscover how bias in AI arises from training data, the impact of English on Dutch output, and practical methods to mitigate bias.
• What is a transformer? The brain behind modern AI, intuitively explained - LLMNet LearningDiscover how the transformer model works behind modern AI. Learn about the attention mechanism, simple analogies, and test your knowledge with a practical exercise.