[PRIMO Tech-a-Break] Making AI Smarter Together with Chain of Context

[PRIMO Tech-a-Break] Making AI Smarter Together with Chain of Context

Have you ever experienced this?

You ask AI a question, but the answer feels off.
Meanwhile, someone else asks the exact same topic, yet gets a much sharper, more accurate, and clearer answer.

I used to wonder why that happens too. Eventually, I realized the difference isn’t about whether one AI is “smarter” than another.
The key lies in how we interact with AI.

Today, PRIMO Tech-a-Break introduces Chain of Context—a technique that helps both AI and us get smarter together.

What is Chain of Context?

In technical terms, Context means the background or information we provide with our question.
Chain means the linking of those questions together.

So when we talk about Chain of Context, it means asking questions step by step, using the previous answer as the foundation for the next one. This way, AI never “forgets” what we’re discussing and keeps building accurate responses.

Part 1: What does asking in a chain look like?

Example with a CRM system:

Me: “Show me last month’s VIP customers.”
AI: (displays data)

Me: “Within this group, which category did they purchase the most?”
AI: “Category A.”

Me: “And what payment method did they use most often?”
AI: “Credit card, 65%.”

Notice how, by asking step by step, the AI continuously understands we’re still focusing on last month’s VIP customers.

Part 2: One-shot Question vs. Chain

One-shot Question

“Which products did VIP customers buy most often, and what payment methods did they use?”

Here, AI has to interpret multiple conditions in one go.
The answer often ends up broad, vague, or lacking detail.

Chain Questioning

  • I get focused answers, one point at a time.
  • I stay in control of the direction—digging deeper wherever needed.
  • The insights are clearer and easier to apply in real-world scenarios.

Why Chain of Context Matters

  1. Reduce Noise – No need to repeat background info, but also no risk of AI guessing incorrectly.
  2. Control Flow – We design the flow of questions instead of dumping everything at once.
  3. Accuracy – More precise answers because AI has clear context.
  4. Structured Thinking – By chaining questions, we practice breaking down complex problems into smaller ones—similar to writing code.

Tips for Using Chain of Context

  • Define Scope First → Start with a frame, e.g., “last month’s VIP customers.”
  • Ask Step by Step → Break it into smaller questions, like “Which category did they buy the most?”
  • Reconfirm Context → If the conversation gets long, gently restate, e.g., “We’re still looking at the VIP group, right?”

Wrapping Up

Asking a one-shot question might get you broad, surface-level answers.
But with Chain of Context, you’ll get sharper insights, step by step, that you can build on.

The difference between using AI and using a regular app is this:
With an app, you’re simply a user—limited to what the app can do.
With AI, if you stay in “just a user” mode, you’ll only get results as if you were chatting with a rule-based chatbot.

But if you learn to structure your thinking alongside AI, your effectiveness multiplies.

In the end, using Chain of Context doesn’t just make AI smarter—it makes me think more systematically too.

So how much value you get from AI really depends on how much you learn to get value with it.