One thing to watch for with Claude & GPT is that the models expose too much irrelevant history in their outputs. Slides are given footers saying things like "Better, more targeted version" if you asked for a better version, documents make references to how they are improved, etc
PROMPT ENGINEERING
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Automated 4-phase reality check workflow for Claude
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Now every time you ask Claude for a reality check, it runs the full 4-phase analysis automatically. No re-pasting. No setup. Just ask and it delivers. Follow @godofprompt for more prompts like this.
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Clear communication as the foundation of effective prompt engineering
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Prompt engineering is just communication with a specific audience. The "best prompt template" won't fix unclear thinking. The "perfect framework" won't fix vague instructions. Learn to communicate clearly, and every model gets better.
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Understanding Model-Specific Prompt Engineering and Audience Analysis
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Theory 3: Audience Analysis (Aristotle to today) The oldest communication principle: know who you're talking to. In prompting, your "audience" is the model. And every model processes differently. Claude reads system prompts differently than ChatGPT. Gemini handles long context
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Guide to AI-Powered Qualitative Data Analysis with ChatGPT
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Qualitative Data Analysis With Chatgpt And Qualcoder: A Step-By-Step Guide To AI-Powered Coding And Thematic Analysis AI-Powered Research Toolkit — Mastering Research Series — available at http://
amzn.to/4bRsV3q -
Google releases beginner-friendly prompt engineering guide
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9/ Prompting Guide 101 – by @Google A beginner-friendly resource to master the fundamentals of prompt design. → https://
services.google.com/fh/files/misc/
gemini-for-google-workspace-prompting-guide-101.pdf
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Anthropic Releases Comprehensive Prompt Engineering Guide
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7/ Prompt Engineering Overview – by @Anthropic A structured guide with best practices, techniques, and tutorials to improve your prompts step by step. → https://
docs.anthropic.com/en/docs/build-
with-claude/prompt-engineering/overview
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Experiments Used GPT-4 and GPT-4o; Newer Models Could Improve Results
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And, yes, our experiments used a mix of GPT-4 & GPT-4o (publishing takes awhile). I think we would see much larger results with more recent models, let alone recent agentic tools.
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Optimizing LLM Prompt Length and Workflow Efficiency
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"How long are your prompts for these?" 600 and 900 words for last two, both assuming refinement after initial delivery. One operator at a single keyboard can do several of them during a work day, even assuming downtime as the LLM goes off and cranks for 5 minutes between revs.
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Using AI agents and headless browsers for automated animation debugging
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Presumably if I were more sophisticated at prompting I could ask it to use a headless browser to render every 1s of the animation to a screenshot then fix all the bugs identified w/o me needing to flag them individually. Tokens are abundant, after all.