Prompt engineering has a reputation for being either trivial ("just ask nicely") or mysterious ("dark art requiring special knowledge"). Neither is accurate. It is a set of learnable techniques grounded in how language models process input.
Be Explicit About Output Format
The single most impactful thing you can do is specify the exact format you want. LLMs default to verbose prose because that is what most training data looks like.
Bad prompt: "Give me the pros and cons of using Redis."
Better prompt: "Give me the pros and cons of using Redis as a session store.
Return your answer as a JSON object with two arrays: 'pros' and 'cons'.
Each item should be a single sentence. Maximum 5 items per array."When you specify JSON output, you can parse it programmatically. When you do not, you are string-splitting hope.
Chain of Thought for Complex Reasoning
For tasks requiring multi-step reasoning, asking the model to "think step by step" measurably improves accuracy. This is not magic — it forces the model to generate intermediate reasoning tokens that inform the final answer.
Prompt: "A developer needs to handle 10,000 concurrent WebSocket connections
on a single server. Think through the key constraints step by step, then
recommend an approach."Few-Shot Examples Beat Long Instructions
Instead of writing elaborate instructions about what you want, show the model examples:
Here are examples of how to format error messages:
Input: "user not found in database"
Output: { "code": "USER_NOT_FOUND", "message": "No account with this email exists.", "status": 404 }
Input: "password too short"
Output: { "code": "VALIDATION_ERROR", "message": "Password must be at least 8 characters.", "status": 422 }
Now format this: "rate limit exceeded"Three examples typically outperform three paragraphs of instructions.
System Prompts for Consistent Persona
When building applications on top of LLMs, use the system prompt to establish role, constraints, and output expectations once — rather than repeating them in every user message. This keeps user prompts focused on actual input and makes behavior more consistent across requests.