We treat AI like a magic 8-ball. We ask it a question, shake it, and hope for a good answer. “Write a report on wheat shortages.” “Predict dengue cases in Lahore.” And when the AI gives us a generic, hallucinated answer, we say, “AI isn’t ready for the real world.” The problem isn’t the AI. It’s the prompt engineering.
For complex problems—the kind Pakistan faces every day—you need a technique called Chain-of-Thought (CoT) prompting. This is the difference between an AI that guesses and an AI that reasons.
Here is how we can use CoT to tackle two massive national issues: Agriculture and Healthcare.
Here are some other prompting ideas.
What is Chain-of-Thought (CoT)?
Standard prompting asks for the answer immediately. CoT forces the AI to “show its work” step-by-step before giving the final answer.
- Standard Prompt: “Will there be a wheat shortage in Punjab this year?” (Result: Generic guess).
- CoT Prompt: “Analyze the rainfall data for the last 3 months, compare it to historical averages, factor in current fertilizer prices, and then conclude if a wheat shortage is likely.” (Result: Reasoned analysis).
Use Case 1: The Wheat Yield Forecast (Agriculture)
The Scenario: A District Commissioner in South Punjab needs to know if they should request extra wheat stock from the federal government.
❌ The Bad Prompt:
“Predict the wheat yield for Rahim Yar Khan district for 2025.”
✅ The Chain-of-Thought Prompt:
“Act as an agricultural data scientist. I need a risk assessment for the 2025 wheat harvest in Rahim Yar Khan.
Think step-by-step:
- Analyze Weather: Review the rainfall data I pasted below for Oct-Dec 2025. Was it sufficient for the sowing phase?
- Input Costs: Factor in the current Urea price of PKR 4,500/bag. How does this correlate with historical usage rates?
- Satellite Data: Based on the ‘Green Cover Index’ data provided, is the crop density higher or lower than the 5-year average?
- Conclusion: Based on steps 1-3, provide a final yield forecast in percentage terms (e.g., +/- 10% of last year).”
The Result: Instead of a guess, the AI connects the high fertilizer cost + low rainfall to predict a 15% yield drop, allowing the DC to order stock now, months before the crisis hits.
Use Case 2: The Dengue Surge Triage (Healthcare)
The Scenario: A government hospital in Lahore is overwhelmed. They have 500 patients and only 50 beds. Who gets admitted?
❌ The Bad Prompt:
“Who should I admit to the hospital?”
✅ The Chain-of-Thought Prompt:
“Act as a Senior Triage Officer. I am providing vital signs for 5 patients. You must decide who gets the ICU bed.
Reasoning Process:
- Check Platelets: Is the count below 20,000? (High Risk).
- Check Warning Signs: Is there bleeding, severe abdominal pain, or rapid breathing?
- Check Comorbidities: Is the patient diabetic or elderly?
- Decision: Rank these 5 patients from ‘Immediate Admission’ to ‘Home Care’ and explain why based on the criteria above.”
The Result: The AI acts as a “second opinion” for tired doctors, ensuring that the patient with the bleeding gums and low BP gets the bed over the stable patient who is just panicking.
Why This Matters
Prompt engineering isn’t just about cheating on your university essay. In a country like Pakistan, where resources are scarce and crises are frequent, Chain-of-Thought prompting turns a chatbot into a consultant.
It forces the system to slow down, think logically, and give answers that can actually save lives.
Have you tried ‘Chain of Thought’ in your work? Try adding “Think step-by-step” to your next prompt and see the difference.