AI Resources, Guides and Explainers
AI is moving fast. This hub collects plain-English guides on large language models, agents, RAG, prompt engineering, and the practical side of building with AI — written for developers who need to ship, not just theorize.
415 articles · Updated July 15, 2026
The fundamentals move slower than the headlines. Understand tokens, embeddings, and context windows once and most new tools will make sense immediately.
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Hand-picked and latest articles from this topic.
Getting started
Beginner-friendly guides to help you learn the fundamentals.
From Explain
Plain-English explainers for core concepts related to this topic.
What is Temperature in LLMs?
Temperature is a setting that controls how random or deterministic a language model's output is, trading off creativity against predictability.
Read guideWhat is AI Hallucination?
AI hallucination is when a model confidently produces information that is false, fabricated, or not grounded in its input.
Read guideWhat is a Diffusion Model?
Diffusion models generate images and other data by learning to reverse a gradual noising process, turning random noise into coherent output.
Read guideWhat is Overfitting?
Overfitting happens when a model memorizes its training data instead of learning general patterns, hurting performance on new data.
Read guideWhat is Reinforcement Learning?
Reinforcement learning trains an agent to make decisions by rewarding good actions and penalizing bad ones through trial and error.
Read guideWhat is Computer Vision?
Computer vision is the field of AI that enables machines to interpret and understand visual information from images and video.
Read guideExternal resources
Curated tools, docs, and communities worth bookmarking.
Frequently asked questions
Quick answers about this topic hub.
What AI topics does In Plain English cover?▾
We cover LLMs, AI agents, retrieval-augmented generation, prompt engineering, model context protocol, and practical tutorials for integrating AI into applications.
Do I need a machine learning background to build with AI?▾
Not to build applications on top of existing models. Working with APIs from providers like OpenAI or Anthropic mostly requires solid software engineering. A deeper ML background matters more if you train or fine-tune models yourself.
What is retrieval-augmented generation (RAG)?▾
RAG combines a language model with a search step: relevant documents are retrieved from your own data and fed into the prompt so the model answers using your content. It's the most common pattern for building AI over private knowledge bases.
