Writing Better Prompts: A Developer’s Superpower 🚀

#promptengineering #developers #productivity #openai

Most developers know this feeling: you ask an AI for help, and the answer is almost right—but not quite. The difference usually isn’t the model. It’s the prompt.

🔑 Core Principles

**Clarity & Scope → **Specify what you want, avoid vague phrasing.
Constraints → Define JSON, table, or summary upfront.
Role Assignment → “Be a code reviewer” or “mentor” sets context.
Step Control → “Reason step by step” keeps logic grounded.

❌ Before vs ✅ After

Weak Prompt:

“Explain microservices.”

Better Prompt:

“Act as a software architect. Explain advantages of microservices step by step. Summarize in 3 bullet points in JSON.”

Output Example:

{
  "advantages": [
    "Each service can be scaled independently.",
    "Teams can work in parallel on different services.",
    "Failure isolation prevents the entire system from crashing."
  ]
}

🧩 Advanced Prompt Structures

When you want less eagerness:

{
  "context_gathering": {
    "goal": "Get enough context fast.",
    "method": [
      "Start broad, then narrow to subqueries",
      "Run parallel queries, deduplicate paths",
      "Avoid over-searching"
    ],
    "early_stop_criteria": [
      "Able to name exact content to change",
      "Top hits converge ~70%"
    ]
  }
}

When you want speed & efficiency:

{
  "context_gathering": {
    "search_depth": "very low",
    "tool_call_limit": 2,
    "bias": "Provide a correct-enough answer quickly",
    "escape_hatch": "Proceed even if not fully certain"
  }
}