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Prompt Engineering: Practical Best Practices That Actually Work

Prompt Engineering overview

Communicating with AI is the same as communicating with Humans. ~ Clarity Helps ~ The quality of output depends heavily on how well the task is explained.

🗣️ Be clear and specific

  • Don’t assume the AI knows what you want.
  • Tell it exactly what to do—no guessing, no ambiguity.

🎭 Asking a model to adopt a persona can help the model use the right perspective to answer your queries.

📋 Show examples and format Providing an example can nudge the model toward the response you want.

📚 Provide enough context When models are given the right reference information, they perform significantly better and are less likely to hallucinate. If the task relates to a document, dataset, or prior discussion—include it. Context matters.

🧩 Break big tasks into small ones Instead of one huge prompt, split it: Step 1: Understand Step 2: Analyze Step 3: Answer

This improves accuracy and structure.

⏳ Give the model time to think Encourage step-by-step reasoning using techniques like chain-of-thought or self-critique prompting. This allows the model to reason more deliberately rather than rushing to an answer.

🔁 Iterate and refine Your first prompt doesn’t have to be perfect. Treat prompts like code—test, improve, repeat.

❌ Unclear prompt:

Analyze our customer feedback.

✅ Well-structured business prompt:

You are a product manager analyzing customer feedback for a SaaS product. Review the feedback provided below and identify the top 3 recurring issues. For each issue, suggest one actionable improvement and its potential business impact. Present the output in a table with columns: Issue, Suggested Action, Expected Impact. Keep the analysis concise and data-driven.

💡 Key takeaway: Prompt engineering is a skill. Clear instructions, sufficient context, and structured thinking dramatically improve the quality and reliability of AI responses.