9 Comments
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Filippo Marino's avatar

Devansh once again delivers a timely masterclass as attention shifts to context engineering. This serves as a critical reminder of the significant impact that prompt design holds in the broader context.

As a side note, we've been increasingly focusing on the 'Role Assignment' pattern for intelligence analysis, crisis management, and forecasting, and found a fascinating Stanford paper titled: "Ask WhAI: Probing Belief Formation in Role-Primed LLM Agents" - it explores the distinction between 'roles' and 'expertise' and how these can lead to different reasoning patterns based on different persona prompt design. Worth reading.

Devansh's avatar

Will check it out. Thank you

ToxSec's avatar

“Self-Reflection and Inversion patterns catch expensive mistakes before implementation. Systematic evaluation reveals overlooked gaps, while external questioning exposes blind spots that internal review misses.”

Couldn’t agree more. The inversion patterns seem to be one of my most powerful prompts all the time. The ability to expose blind spots when you challenge the bot to do so is amazing.

Elle Light 💎's avatar

Great breakdown of prompt patterns, framed as systems with examples and not isolated "tricks".

Devansh's avatar

thank you

Hodman Murad's avatar

I like how this gives you a repeatable way to solve common problems. Instead of guessing why the output is bad, you have a specific move to make.

Önsel Akın's avatar

Looks like another AI generated content. That em-dashes 🤦🏻‍♂️

Aayush Jain's avatar

Great read Devansh. As a proponent of leverage loops myself. I wrote this piece that might benefit some of your readers.

https://open.substack.com/pub/projectaignite/p/the-operators-edge-in-an-ai-hype?r=528d5n&utm_medium=ios&shareImageVariant=overlay

Pranjal Gupta's avatar

Exactly. The production experience validates this.