The PlainLogic blog
Technology news and explainers in plain logic
Timely technology updates and evergreen explainers in plain words, each linked to a hands-on experiment. No hype, no jargon, no account needed.
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The newest updates and explainers, in plain words.
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EXPLAINER
What is temperature? The AI's creativity dial
Temperature is the AI setting that controls randomness: low means predictable answers, high means surprising ones. Here's how it works.
Read the guide →EXPLAINER
What is a context window? Why AI 'forgets' long conversations
A context window is an AI's working memory — the text it can consider at once. When chats outgrow it, earlier parts drop out. Here's why.
Read the guide →EXPLAINER
What is fine-tuning? Teaching AI your playbook
Fine-tuning is extra training that teaches an AI your style, format, or behavior — not new facts. How it differs from prompting, explained.
Read the guide →AI
AI
Plain-words pieces filed under AI.
EXPLAINER
What is temperature? The AI's creativity dial
Temperature is the AI setting that controls randomness: low means predictable answers, high means surprising ones. Here's how it works.
Read the guide →EXPLAINER
What is a context window? Why AI 'forgets' long conversations
A context window is an AI's working memory — the text it can consider at once. When chats outgrow it, earlier parts drop out. Here's why.
Read the guide →EXPLAINER
What is fine-tuning? Teaching AI your playbook
Fine-tuning is extra training that teaches an AI your style, format, or behavior — not new facts. How it differs from prompting, explained.
Read the guide →EXPLAINER
What is chain-of-thought? Why 'think step by step' works
Chain-of-thought means asking AI to show its work before answering. 'Think step by step' measurably improves reasoning — here's why it works.
Read the guide →EXPLAINER
What is tool use? How AI models take action
Tool use lets AI models call APIs and software instead of only writing text. Here's how the tool-call loop works.
Read the guide →EXPLAINER
What are AI evals? How models get tested before release
AI evals test models the way unit tests test code: inputs in, graded outputs, a score. How they work and why they matter.
Read the guide →EXPLAINER
What is RLHF? The training step that made chatbots polite
RLHF trains chatbots on human preferences: people rank answers, a reward model learns taste, the model optimizes for it. The technique behind polite AI.
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What is inference? Running a trained model vs. training it
Inference means running a trained AI model on new data. How it differs from training, and why it drives AI costs.
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What are transformers? The architecture behind modern AI
Transformers power modern AI. What the architecture is, how self-attention works, and why it changed everything — in plain language.
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What is grounding? Connecting AI answers to real sources
AI grounding connects answers to real sources you can check. How grounding reduces hallucinations, in plain language.
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What is a system prompt? The hidden instructions behind every AI reply
A system prompt is the hidden instruction set shaping every AI reply. What it is, who writes it, and why AIs answer differently.
Read the guide →EXPLAINER
What is multimodal AI? Models that see, hear, and read
Multimodal AI processes text, images, audio, and video together. How it works, what it's good for, and its limits — in plain language.
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What is prompt injection? Hijacking AI with hidden instructions
Prompt injection smuggles hidden instructions into AI apps through data they read. How it works, why there's no real fix, and what defenses help.
Read the guide →EXPLAINER
What is model distillation? Teaching small AI models to think big
Model distillation trains a small, fast AI model to imitate a big one's judgment. Learn how the teacher-student trick behind on-device AI actually works.
Read the guide →EXPLAINER
What is an agentic workflow? The loop that lets AI act on its own
An agentic workflow lets an AI plan, act with tools, and observe results in a loop. Learn how the pattern behind AI agents works — and when not to use it.
Read the guide →EXPLAINER
What is supervised learning? How AI learns from labeled examples
Supervised learning trains AI on labeled examples — inputs paired with right answers. How it works, where it's used, and why memorization isn't learning.
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What is an LLM?
An LLM is not a search engine or a person in a box. It is a machine that learned the shape of language and writes text one token at a time. Plain words.
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What are tokens?
AI models don't read words the way you do. They read tokens: fragments of text sliced by a fixed rule. What tokenization means, in plain words.
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What is RAG?
Retrieval-augmented generation gives an AI model documents to read before it answers. How RAG works, what it fixes, and where it still goes wrong.
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What are embeddings?
Embeddings turn words, images, and ideas into lists of numbers so a computer can compare meaning, not just spelling. The simple explanation of semantic search.
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AI agent vs chatbot
A chatbot talks; an agent works. The real distinction between a chat interface and an agent loop — and why the two keep getting mixed up.
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Why AI hallucinates
Language models predict text, not truth. Why confident wrong answers happen, what causes them, and how to check the answers that matter.
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How to write a clear prompt
A useful prompt states four things: the task, the context, the constraints, and the output format. What works, what doesn't, and what no prompt can fix.
Read the guide →How Technology Works
How Technology Works
Plain-words pieces filed under How Technology Works.
How PlainLogic covers technology news
PlainLogic uses automation to monitor technology updates and assist with research and drafting. Articles are built from cited sources and checked for factual consistency before publication.
Every guide and update pairs with a hands-on experiment. The demos are simplified educational visualizations — they illustrate concepts, not real model internals.