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.
“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.
Thank you for this post. Getting to it late, and just checking for a reference mentioning this topic. The book version of this chapter notes "this section is taken from Chapter 5 of AI Engineering in Practice, by Richard Davies." Is that correct for this blog version as well? There seems to be a lot of duplicated text and I want to give due credit. I have for the two:
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.
Will check it out. Thank you
“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.
Great breakdown of prompt patterns, framed as systems with examples and not isolated "tricks".
thank you
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.
Looks like another AI generated content. That em-dashes 🤦🏻♂️
Thank you for this post. Getting to it late, and just checking for a reference mentioning this topic. The book version of this chapter notes "this section is taken from Chapter 5 of AI Engineering in Practice, by Richard Davies." Is that correct for this blog version as well? There seems to be a lot of duplicated text and I want to give due credit. I have for the two:
Devansh Devansh (Pasha). (2026, February 18). 10x your AI with these 9 Foundational Prompt Patterns: AI Engineering at Scale part 2 [Substack]. Artificial Intelligence Made Simple. https://www.artificialintelligencemadesimple.com/p/10x-your-ai-with-these-9-foundational
Richard Davies. (2025). Prompt patterns and templates (from AI Engineering in Practice Ch.5). In Devansh Devansh (Pasha) & Manning (Eds.), Engineering AI at Scale: Platforms, RAG, Agents, and Reliability (Online). Manning Publications. https://blog.manning.com/engineering-ai-at-scale-platforms-rag-agents-and-reliability
Cheers,
Leo
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
Exactly. The production experience validates this.