Model-Agnostic Prompting Methods (Works on All Platforms)

You don't need to learn code. You just need to structure your written instructions clearly. Use these two reliable, professional frameworks:

Method 1 — Bracket Labeling (Structured Prompting)

AI performs drastically better when you isolate your instructions from your data using clear labels. This prevents the model from "losing" parts of your request in long text.

The Template:

[ROLE]

You are a senior policy analyst specializing in Southeast Asian food security and agricultural supply chains.

[CONTEXT]

[Paste your 20-page stakeholder or regional crop report here]

[INSTRUCTIONS]

1. Summarize this document into 5 actionable bullet points tailored for the Minister.

2. For each bullet point, cite the specific section or page it was drawn from.

3. Flag any claims in the text that appear unsupported by hard data.

[OUTPUT FORMAT]

5 bullet points, each starting with an action verb, total length under 200 words.

Method 2 — "Think First, Then Answer"

For heavy policy work, forcing the AI to explicitly write out its logic before giving you the final answer dramatically reduces errors and hallucinations.

The Template:

Before you provide the final answer, list the underlying assumptions present in this policy draft.

Then, indicate whether there is sufficient evidence in the public domain to support each assumption.

Finally, give your concise assessment of the draft's overall strategic soundness.

Further Reading

• Anthropic Context Engineering Framework — A highly readable guide on why structuring prompts logically yields superior strategic results.
• Kimi Prompt Best Practices — Practical prompt engineering guide from the Kimi team with real examples and patterns.