AI coding assistants like GitHub Copilot feel like magic, but they are far more useful once you understand how they work and how to teach them your project. This series starts from nothing: how Copilot reads your code, then the files you use to customise it — custom instructions, prompt files, instructions files, skills, and custom agents — and ends with prompting principles that carry across every tool and the patterns that get great results on real coding work. Every term is spelled out the first time it appears.
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Article 1
How GitHub Copilot Works, Explained From Zero
What an AI coding assistant actually does — the model behind it, how it reads your open files as context, the difference between inline completions, chat, and agent mode, and why what it can see decides what it can do.
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Article 2
Custom Instructions, Explained From Zero
The file that teaches your assistant about your project — how a short document of conventions gets added to every request automatically, so you stop repeating yourself and it stops guessing.
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Article 3
Prompt Files, Explained From Zero
Saving a good prompt as a reusable file — how a prompt you perfected once becomes a one-click command with inputs, and how sharing them turns your best prompts into team infrastructure.
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Article 4
Instructions Files & applyTo, Explained From Zero
Scoping guidance to the right part of your codebase — how path-based instruction files apply different rules to frontend, backend, and tests, so the assistant gets exactly the context that fits.
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Article 5
Skills (SKILL.md), Explained From Zero
Packaging expert knowledge the assistant loads only when needed — how a skill is a folder the agent reaches for on demand, keeping deep know-how ready without clogging every request.
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Article 6
Custom Agents & Chat Modes, Explained From Zero
Building your own specialised assistant — a named mode with its own instructions and its own limited set of tools, so you get a focused, safe expert for a job instead of one all-purpose helper.
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Article 7
Prompting Across Tools, Explained From Zero
Why the same principles work in Copilot, Cursor, Claude, and ChatGPT — the shared foundation under every tool, the config file each uses to learn your project, and how to carry your skills from one to the next.
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Article 8
Writing Prompts for Coding Tasks, Explained From Zero
The practical patterns that get great results on real coding work — give the right context, break big jobs into steps, ask for tests, state constraints, and review everything.
More in the AI Security collection
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Companion series
AI Security
Sixteen zero-assumed-knowledge explainers of AI agent security — what an agent is, prompt injection, excessive agency, guardrails, the OWASP Top 10, and MITRE ATLAS.
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Companion series
Prompt Engineering
The craft of talking to AI models — writing clear prompts, message roles, teaching by example, chain-of-thought reasoning, structured output, and the temperature dial.
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Companion series
Building AI Apps
The building blocks of real AI applications — embeddings, RAG, chunking, tool calling, the Model Context Protocol, context engineering, and agent memory.
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Companion series
AI Quality & Delivery
Making AI features good and shippable — hallucinations, grounding, evaluations, the prompting-vs-RAG-vs-fine-tuning decision, model selection, and cost.