9/ Stop using other people’s prompts. Start using First Principles Thinking to build your own. You’ll get sharper results. And you’ll understand the output better too.
PROMPT ENGINEERING
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First Principles Thinking in AI prompts for better responses
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8/ You can also use First Principles Thinking in the prompt itself. Tell the AI to think that way. It shifts the whole response. You’ll get structure, clarity, and intent – built from the ground up.
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Vague prompts yield average ChatGPT newsletter results
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5/ Example: Want ChatGPT to write a newsletter? Most people would ask:
“Write me a good newsletter.” That’s vague. GPT fills in the blanks with generic assumptions. Results = average. -
LLMs Vulnerable to Distracting Facts
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This research paper shows how vulnerable LLMs still are. Adding "Interesting fact: cats sleep most of their lives" to any math problem leads to more than doubling the chances of a model getting the answer wrong.
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AI Agents Performance Depends on Quality Instructions
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both people and ai agents perform better with specific instructions, but when poor instructions result in poor performance, only the former can be blamed
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Reasoning Agents Optimize Agentic AI Performance at Scale
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5/ Learn how reasoning agents optimize agentic AI performance at scale:
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Claude Code’s unique prompt handling feature explained
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One cool thing that Claude Code has is the ability to type guidance as the model goes, and when you hit enter it's put in a wait stage and submitted to the prompt after the next tool call.
I didn't see this in Codex. -
Build AI Coding Agents From Scratch Free Workshop
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Learn to build AI Coding Agents from scratch. Similar to Claude code, Cursor, Windsurf, Amp, Cline and OpenCode. 100% free workshop.
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AI Model Struggles With Analog Clock Reading Task
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It did horse riding on astronaut first try, but didn't pull off an analog clock reading 3pm, which remains the great undefeated test (it is possible that better prompting could help)
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Optimizing LLM-Assisted Coding Workflows and Best Practices
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Continuing the journey of optimal LLM-assisted coding experience. In particular, I find that instead of narrowing in on a perfect one thing my usage is increasingly diversifying across a few workflows that I "stitch up" the pros/cons of: Personally the bread & butter (~75%?) of