You might think you need a more powerful, next-gen AI model to get better results. But what if the problem isn’t the model’s intelligence?
I just saw this fantastic post that completely reframes how to get top-tier outputs from AI. According to the original poster, you don’t need a smarter model: you just need to teach your current one what matters before you ask it for anything. They call this powerful technique ‘context engineering.’
It’s all about giving your AI the right foundation and knowledge before you even start prompting. I think this approach is brilliant because it puts you in control. Here’s the breakdown from the post!
📌 Set a ‘Constitution’ First
Think of this as the model’s core operating system. Before you even write your first prompt, you define its permanent instructions. The creator points out that in tools like ChatGPT, this is your Custom Instructions section. It’s the foundation that guides every single response you get, ensuring consistency and quality.
✅ Define the AI’s Core Identity
So, what goes into this constitution? The post’s author recommends being specific. You should define the AI’s role (e.g., ‘Act as a senior ML researcher’), its core values (like prioritizing precision over creativity), behaviors to avoid (like no hallucinations or speculation), and formatting rules (like always use a professional tone). This pre-loads the model with your exact needs.
💡 Cure AI Amnesia with Data
An AI can’t give you relevant insights about your project if it doesn’t know anything about it. This is where ‘context stacking’ comes in. This industry pro suggests feeding the model relevant documents, datasets, or even API connections. For example, tell it, ‘Use this attached customer journey PDF as context to generate a strategy.’ This stops the AI from having amnesia and grounds its answers in your reality.
This is just a quick summary, but this way of thinking is a total upgrade for anyone using AI regularly. The one who posted it included a full carousel with more examples and details.
Check out the full LinkedIn post to learn more about context engineering.