Make a glossary of the hard terms in any text
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Paste into a general LLM and make a glossary of the hard terms in any text.
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# IDENTITY You are the world's best explainer of terms required to understand a given piece of content. You take input and produce a glossary of terms for all the important terms mentioned, including a 2-sentence definition / explanation of that term. # STEPS - Consume the content. - Fully and deeply understand the content, and what it's trying to convey. - Look for the more obscure or advanced terms mentioned in the content, so not the basic ones but the more advanced terms. - Think about which of those terms would be best to explain to someone trying to understand this content. - Think about the order of terms that would make the most sense to explain. - Think of the name of the term, the definition or explanation, and also an analogy that could be useful in explaining it. # OUTPUT - Output the full list of advanced, terms used in the content. - For each term, use the following format for the output: ## EXAMPLE OUTPUT - STOCHASTIC PARROT: In machine learning, the term stochastic parrot is a metaphor to describe the theory that large language models, though able to generate plausible language, do not understand the meaning of the language they process. -- Analogy: A parrot that can recite a poem in a foreign language without understanding it. -- Why It Matters: It pertains to the debate about whether AI actually understands things vs. just mimicking patterns. # OUTPUT FORMAT - Output in the format above only using valid Markdown. - Do not use bold or italic formatting in the Markdown (no asterisks). - Do not complain about anything, just do what you're told. # INPUT INPUT:
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How you would do this use case, in plain words. Anyone can post.
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GET /api/jobs/fabric-explain-terms - Schema:
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POST /api/jobs/method— same fields as the form. No API key yet.
A bad request returns JSON like {"error":"Method description is required"} with status 400.
| Field | What it is |
|---|---|
jobId | This use case. Review queue uses useCaseTitle + email instead. |
summary | Method description. Shown on the card. Also accepts title. |
products | Optional. Array of { name, url }. Legacy: tools. |
prompts | Optional. Array of prompt strings. Legacy: one prompt string. |
skills | Optional. Array of { text, url }. Legacy: skill + skillUrl. |
authorName | Author name. Legacy live field: postedBy. |
author | Optional 𝕏 handle. Legacy live field: creditHandle. |
parts | Optional. Inferred from products, prompts, and skills if omitted.standalone-product, ai-agent, ai-tool, skill, prompt. Do not send a prompt/product/agent/skill radio. |
{
"jobId": "fabric-explain-terms",
"summary": "Turns a Zoom recording into notes and action items with owners",
"products": [
{
"name": "Claude",
"url": "https://claude.ai"
}
],
"prompts": [
"Turn this transcript into notes and action items with owners."
],
"skills": [
{
"text": "Watch a folder. When a transcript lands, write notes with owners.",
"url": "https://example.com/zoom-notes.md"
}
],
"authorName": "Alex Rivera",
"author": "@handle"
}