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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteThese three resources approach prompt engineering from different angles: a broad cheat sheet, a role-based guide for Gemini in Google Workspace, and a Python library for compressing prompts. They were highlighted by Matthew Mayo in a KDnuggets article published May 1, 2024; treat that date as the point-in-time description, not confirmation that each resource remains available or maintained today.
Which resource fits your task?
| Resource | Best fit | What it covers or does | Technical commitment | Current availability |
|---|---|---|---|---|
| The Prompt Engineering Cheat Sheet | Readers who want a broad prompt-writing reference | Frameworks, examples, output formats, templates, RAG, and multi-prompt approaches | Reference material; no coding requirement is stated | Not established by the May 1, 2024 KDnuggets article |
| Gemini for Google Workspace Prompt Guide | People applying prompts to roles and everyday tasks in Google Workspace | Role- and use-case-oriented prompt guidance for Gemini in Workspace | Designed for Workspace users; no coding requirement is stated | Not established by the May 1, 2024 KDnuggets article |
| LLMLingua | Developers exploring prompt compression | A Python library that aims to remove tokens considered non-essential | Software and code, rather than a general beginner handbook | Not established by the May 1, 2024 KDnuggets article |
The comparison reflects the descriptions in Mayo’s KDnuggets article. It does not establish current setup steps, compatibility, effectiveness, or whether the resources can still be accessed.
The Prompt Engineering Cheat Sheet: a broad reference
KDnuggets credits the cheat sheet to Maximilian Vogel and The Generator and describes it as covering prompt engineering from basic prompting through retrieval-augmented generation (RAG). It is the most general-purpose option of the three if you want to browse techniques rather than follow guidance for one specific product or implement a compression method.
Its listed subjects include the AUTOMAT and CO-STAR frameworks, defining output formats, few-shot learning, chain-of-thought prompting, prompt templates, RAG, formatting and delimiters, and multi-prompt approaches. The article says a PDF version is available, but it does not establish whether that file or the sheet itself remains accessible.
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Gemini for Google Workspace Prompt Guide: prompts by role and task
This guide is aimed specifically at people using Gemini in Google Workspace. The article describes it as a quick-start handbook organized around roles and everyday use cases, making it the closest fit if your goal is to apply prompts to routine Workspace work rather than study a broad collection of techniques.
Mayo suggests that some of its advice may apply beyond Workspace, but that is the article author’s characterization; the guide’s central audience remains Workspace users. Its reproduced description says it explores ways to get started and learn the basics for day-to-day tasks. The 2024 article does not verify the guide’s current location or availability.
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LLMLingua: a developer route to prompt compression
LLMLingua is described as a Python library based on Microsoft’s LongLLMLingua paper. The project explanation reproduced by KDnuggets says it uses a compact, trained language model—examples given are GPT2-small and LLaMA-7B—to identify and remove tokens considered non-essential in prompts. The aim is to reduce prompt length, with potential cost and latency benefits, while retaining response quality.
The KDnuggets article repeats a claim of “up to 20x compression with minimal performance loss.” That is a claim as presented in the May 1, 2024 article, not a guaranteed result for every prompt or workload. The cited account does not provide the conditions behind the figure or verify current software compatibility, so developers should consult the project’s current documentation and evaluate compression on their own tasks before relying on a particular reduction or quality outcome.
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How to choose
- Choose the cheat sheet if you want a wide-ranging catalog of concepts and techniques to consult while writing prompts.
- Choose the Gemini guide if your immediate work is in Google Workspace and you want guidance organized around roles and practical use cases.
- Explore LLMLingua if you are comfortable with a Python library and specifically want to investigate shortening prompts programmatically.
These are different kinds of resources, not interchangeable courses or competing tools. The 2024 article does not compare their effectiveness or document their present maintenance status. Check each destination before downloading or integrating anything, and for LLMLingua verify the current installation and compatibility instructions directly.
Quick Recap
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