Explain Complex Ideas Clearly: A Reusable Workflow Inspired by the ELI5 Skill

AI explanations often fail because they do not decide what to leave out. Ask what RAG is and you may get embeddings, vector dimensions, indexing parameters, and a long definition—without learning what problem it solves in a product.

The public ELI5 Skill in Anthropic’s Claude Plugins Community has a simple brief: explain a topic to someone who knows nothing about it in an HTML artifact with big pictures and few words. It does not prescribe a topic or a conclusion. It does impose a useful discipline: an explanation is not compressed research; it is research reorganized for a particular reader.

The workflow above has six layers. Each layer should answer a new reader question. If one is missing, more terminology will not repair the explanation.

Define these four inputs before asking AI to make it simpler

“Explain this simply” provides almost no instruction. Give the model these inputs instead.

Without those constraints, a model often hides uncertainty behind breadth. It adds definitions, advantages, and jargon. A clear “outside this guide” boundary is more useful to a beginner.

Build the explanation in five editorial passes

Start with the job it performs

Write one sentence, preferably under 30 words, that says what the concept saves someone from doing. Do not start with a textbook definition.

• Weak: RAG is a technical approach that combines retrieval and generation.
• Useful: RAG finds evidence in your material before the model answers from it.

The next sentence supplies the situation: it matters when a support answer must follow a current knowledge base rather than rely only on the model’s memory.

Use one analogy, then name its limit

An analogy is a bridge, not the mechanism. RAG can be introduced as an open-book exam: the model looks through selected material before answering. Then state the limit: retrieved material can still be stale, irrelevant, or contradictory. Open-book does not mean automatically correct.

This prevents two common failures: presenting a metaphor as the system itself, or giving a metaphor with no operational meaning.

Walk through one inspectable example

Use one input, one process, and one output instead of ten industry examples.

Question: When will a refund arrive?
Search scope: published support rules from the last 90 days
Evidence returned: refund timing policy and holiday exceptions
Answer rule: cite matched policy; hand off when no policy matches

Now the reader can see where retrieval happens and what constrains the answer. If a value is illustrative, label it. Do not present a made-up dashboard or metric as a real result.

Let the visual explain structure

“Big pictures and few words” means that an image should explain relationships while prose explains judgment. Put sequence, hierarchy, inputs, outputs, and trade-offs in a diagram. Keep conditions, exceptions, numbers, and decisions in readable text.

One visual should answer one question: what happens first, how two parts connect, or why a choice is made. Keep node labels short and explain the “why” in the sentences around the diagram.

End with a boundary and one next action

A trustworthy explanation says when it should not be used. RAG cannot compensate for missing permissions, stale documents, or an undefined update process. End with an action the reader can take today: test ten real questions, record why retrieval missed, and decide whether to improve documents or retrieval rules.

Copy this prompt to create an explainer page

You are an explanatory editor for [reader type]. Create a standalone HTML page about [topic].

What the reader already knows: [knowledge]
What they should be able to do after reading: [action]
Verified material and evidence boundary: [links, source material, or verified facts]
Out of scope: [exclusions]

Requirements:
1. Open with one sentence (32 words or fewer) stating who it helps and what job it does.
2. Use one everyday analogy and immediately state where that analogy breaks.
3. Show one mini-case from input to output; label unknown values [VERIFY].
4. Include one SVG or HTML process diagram; each node is eight words or fewer.
5. Add two “do not use this when” boundaries.
6. Finish with one verification action the reader can complete today.
7. Never invent figures, citations, product capabilities, or customer examples.

For an HTML deliverable, also ask for mobile-readable type, a constrained text width, strong contrast, and scalable SVG or CSS diagrams. Do not bake tiny paragraphs into an image: readers should be able to search, copy, and have the page read aloud.

Review the result with this checklist

Have someone unfamiliar with the topic spend three minutes with the page. They should be able to say what it solves, what it needs, where it fails, and what they should do next. If two answers are missing, rewrite the reader inputs and mini-case before polishing the prose.

Put the rule into your regular AI workflow

Save the prompt as a team template and require reader, action, scope, and evidence fields for every explainer. When you access models through Code0, those fields can also form a stable request structure; model and endpoint availability should always be confirmed in the current console. The goal is not to make an explanation sound clever. It is to let a reader repeat it, verify it, and act on it.

This guide is based on public project material and is not an official Anthropic or third-party announcement. Check the relevant repository and product documentation for current project contents and availability.