Tool Use: When the Model Calls Something Outside Itself
A plain-language explanation of how AI tool use extends what a model can do by connecting it to external capabilities, with examples, limits, and an anti-hype check.
Topic
ai literacy
17 articles explore this thread.
Articles in this topic
Retrieval-Augmented Generation: Looking Things Up Before Answering
A plain-language guide to retrieval-augmented generation: what it is, when it helps, why it sometimes fails, and what older ideas it builds on.
Reasoning Models: Slower Thinking, Better Checks?
A plain-language explanation of reasoning models: how they use extra computation to work through problems step by step, and where the real limits lie.
Prompt Engineering: Instruction Design, Not Magic Words
A plain-language guide to prompt engineering: how clear instructions, examples, and constraints shape AI outputs, and why it is design rather than magic.
Prompt Caching: Reusing Stable Context
A plain-language guide to prompt caching: what it reuses, why providers offer it, where the savings are real, and what builders should check before relying on it.
Planning and Reflection: How AI Breaks Down and Revises Work
A plain-language explanation of planning and reflection in AI agents, showing how systems break work into steps, check their own output, and revise before continuing.
Multi-Agent Systems: When More Than One AI Worker Is Involved
A plain-language explanation of multi-agent systems: how multiple AI workers are assigned different roles, how they coordinate, and where the design tradeoffs really matter.
Memory vs Context: What Should Survive the Conversation?
A plain-language explanation of the difference between context and memory in AI systems, with everyday analogies, practical examples, and clear limits.
Loops vs Goals: The Difference Between Repetition and Direction in AI Agents
A plain-language explanation of why AI agents need both loops and goals, with everyday analogies, practical examples, and clear limits.
Long-Running Sessions: Keeping AI Work Coherent Over Time
A plain-language guide to what makes AI sessions stay coherent across long tasks, where they drift, and how to keep them on track.
Fine-Tuning: Teaching a Model a Narrower Behavior
A plain-language guide to fine-tuning: what it changes, how it differs from prompting and retrieval, where it helps, and where the hype overpromises.
Evaluations: How We Know an AI Workflow Improved
A plain-language guide to AI evaluations: what they measure, how to design them, and why a good score does not always mean a useful system.
Context Management: What the AI Sees Right Now
A plain-language guide to context management: how language models choose what goes into their working window, why it matters, and where the limits lie.
AI, De-Mystified: A Field Guide to Modern AI Terminology
The guide article for the AI, De-Mystified series, introducing the series promise, article order, and how to read the articles.
Agents: Goal-Directed AI Systems That Use Tools
A plain-language guide to what AI agents are, how they combine goals, tools, loops, and memory, and where the current hype overstates their autonomy.
You Do Not Need to Learn AI First: A 5-Minute Conversation Recipe
A short, practical onboarding recipe that lets non-technical adults and teens start using AI agents by copying one prompt into any capable model, with worked examples for explaining a utility bill and turning meeting notes into summary, email, and action items.
Beyond the First Conversation: Advanced Questions for New AI Agent Users
A practical follow-up for non-technical readers who have tried an AI agent once or twice. It covers privacy limits, recovering from wrong answers, trust, better prompts, small automations, choosing a model, and five safe practice conversations.