What You Will Learn

In this article, you will learn:

• What prompt engineering is.
• How to write clear prompts.
• How roles, context, examples, and constraints improve output.
• How to request structured output.
• How to evaluate prompts.

Introduction

Prompt engineering is the practice of designing inputs that help an AI model produce useful output.

A prompt can include:

• Role.
• Task.
• Context.
• Constraints.
• Examples.
• Output format.
• Quality rules.

Basic Prompt Structure

Role: You are a senior Java architect.
Task: Explain RAG to a Spring Boot developer.
Context: The reader knows REST APIs and databases.
Output: Use bullet points and one simple diagram.
Constraints: Keep it beginner-friendly.

Why Prompts Matter

LLMs are flexible. Without clear instructions, output can be vague, inconsistent, or too broad.

Good prompts improve:

• Accuracy.
• Format consistency.
• Relevance.
• Tone.
• Safety.
• Evaluation.

Prompt Pattern

flowchart TD
    A["Role"] --> B["Task"]
    B --> C["Context"]
    C --> D["Constraints"]
    D --> E["Output format"]
    E --> F["Model response"]

Use Clear Instructions

Weak prompt:

Explain AI.

Better prompt:

Explain Generative AI to a Java backend developer in 5 bullet points. Include one enterprise use case and one risk.

Add Context

Context helps the model understand the situation.

Example:

We are building a customer support assistant for insurance claims. Explain how RAG can reduce hallucinations.

Provide Examples

Examples improve formatting and behavior.

Return output like this:
{
  "summary": "...",
  "riskLevel": "LOW|MEDIUM|HIGH",
  "nextAction": "..."
}

Set Constraints

Useful constraints:

• Use only the provided context.
• Do not guess.
• Return JSON only.
• Keep answer under 200 words.
• Ask a clarification question if required data is missing.

Prompt Engineering for RAG

For RAG, the prompt should tell the model how to use retrieved context.

Example:

Answer using only the provided context. If the answer is not in the context, say "I do not know based on the provided documents."

Evaluate Prompts

Prompt quality should be tested with real examples.

Check:

• Does the answer follow the format?
• Is it grounded in context?
• Does it avoid guessing?
• Does it handle missing data?
• Does it stay within policy?

Interview Questions

What is prompt engineering?

Prompt engineering is the practice of writing clear instructions and context so an AI model produces useful, reliable output.

What makes a good prompt?

A good prompt has a clear task, relevant context, constraints, examples, and an expected output format.

Why is prompt evaluation important?

Evaluation confirms that prompts work reliably across realistic inputs, not just one happy-path example.

Summary

Prompt engineering is a practical skill for building LLM applications. Clear instructions, context, examples, constraints, and evaluation make model output more reliable.