Mastering Prompt Engineering for Developers: 6 Essential Prompting Techniques (Part 1)

Have you ever asked an AI to do something, and it gives you... well, something completely weird? You're not alone. Getting a Generative AI or a Large Language Model (LLM) to give you the exact, perfect answer is an art and a science.

That science is called Prompt Engineering.

If you're a developer, a writer, or just someone curious about AI, mastering this skill is like learning the "magic words" to unlock the AI's true potential. In this post (Part 1 of our series!), we're going to dive into the 6 foundational techniques that will immediately improve your results. And since many of us are developers, we'll use Java examples to make it real.

Let's get started.

What is Prompt Engineering, Anyway?#

Think of a Generative AI as an incredibly smart, incredibly fast intern. It knows everything... but it has no idea what you specifically want.

Prompt Engineering is the process of designing and refining your instructions (the "prompt") to guide the AI to a high-quality, relevant, and accurate response.

It’s the difference between saying "Write code" and "Write a thread-safe Java method that implements a singleton pattern using double-checked locking." The second prompt will always win.

Here are the first 6 techniques you need to know.

1. Zero-Shot Prompting: The "Just Do It" Command#

What is it? This is the simplest form of prompting. You give the AI a task without any prior examples. You are "shooting" for an answer with zero setup. The AI has to rely entirely on its vast training data to figure out what you mean.

Why It’s Important: This is your baseline. It's perfect for straightforward, general knowledge tasks. It's a great way to test the AI's general capabilities and see if it can handle a request "cold."

Real-World Java Example: You need a simple utility function and don't want to waste time providing examples.

Prompt:

"Write a Java method that calculates the factorial of a number."

Result (from the AI):

Java

public class MathUtil {
    public static long factorial(int n) {
        if (n < 0) {
            throw new IllegalArgumentException("Factorial is not defined for negative numbers.");
        }
        if (n == 0 || n == 1) {
            return 1;
        }
        long result = 1;
        for (int i = 2; i <= n; i++) {
            result *= i;
        }
        return result;
    }
}

2. One-Shot Prompting: The "Here's a Hint" Command#

What is it? This time, you give the AI one single example to guide it. You show it the pattern or format you want it to follow. The AI uses your example as a reference.

Why It’s Important: Use this when you have a specific format in mind. It's incredibly useful for style matching, code refactoring, or simple pattern recognition. It’s like saying, "See this? Do something just like it."

Real-World Java Example: You have a utility class and want to add a new method that follows the existing style.

Prompt:

"Here’s a Java method to square a number. Write a method to cube a number:

public int square(int num) { 
    return num * num; 
}

Result (from the AI):

Java

public int cube(int num) {
    return num * num * num;
}

3. Few-Shot Prompting: The "See the Pattern?" Command#

What is it? If one example isn't enough, you give a small set of examples (usually 2-5). This helps the AI understand more complex patterns, nuances, or a specific way of thinking.

Why It’s Important: This is your go-to when the task is more complex or ambiguous. By showing the AI a few instances of the problem and solution, you dramatically increase the chances it will "get" the logic you're aiming for.

Real-World Java Example: You're building a calculator class and want the AI to understand the pattern for adding new operations.

Prompt:

"Here are a few methods in my Java utility class. Write a method to multiply two numbers:

public int add(int a, int b) {
    // Adds two integers
    return a + b;
}

public int subtract(int a, int b) {
    // Subtracts the second integer from the first
    return a - b;
}

Result (from the AI):

Java

public int multiply(int a, int b) {
    // Multiplies two integers
    return a * b;
}

Note: The AI correctly inferred the pattern, including the return type, parameters, and even the style of the code comment.

4. Chain-of-Thought (CoT) Prompting: The "Show Your Work" Command#

What is it? This is a game-changer. Instead of just asking for the answer, you ask the AI to reason through the problem step-by-step. You prompt it to "think out loud" before giving the final solution.

Why It’s Important: CoT prompting is essential for complex logic, math problems, or debugging. It forces the AI to slow down and analyze the problem logically, which drastically reduces errors. It also allows you to see its reasoning and spot any flaws.

Real-World Java Example: You need to solve a logic-based coding problem, not just a simple utility.

Prompt:

"I need to write a Java method that calculates the sum of all even numbers in a List<Integer>. First, explain the steps you would take, and then write the Java code."

Result (from the AI):

`"Certainly. Here are the steps to solve this problem:

1. Define the Method: We'll create a public method called sumOfEvens that accepts a List<Integer> called numbers.
2. Initialize a Sum: We need a variable, sum, to store the total. We'll initialize it to 0.
3. Iterate the List: We'll loop through each number in the numbers list.
4. Check for Even: Inside the loop, we'll use the modulo operator (%). If number % 2 == 0, it means the number is even.
5. Add to Sum: If the number is even, we add it to our sum variable.
6. Return the Sum: After the loop finishes, we return the final sum.

Here is the Java code based on those steps:`

import java.util.List;

public class ListProcessor {
    public int sumOfEvens(List<Integer> numbers) {
        int sum = 0;
        for (int number : numbers) {
            if (number % 2 == 0) {
                sum += number;
            }
        }
        return sum;
    }

5. Contextual Augmentation: The "Here's the Background" Command#

What is it? This technique involves providing the AI with additional background information or "context" about your project or task. You're "augmenting" (or adding to) the prompt with relevant details.

Why It’s Important: The AI doesn't know your project's architecture, your coding standards, or your business goals. Use this when the AI's general-purpose answer isn't specific enough. By providing context, you get a response that fits your specific needs.

Real-World Java Example: You're working in a Spring Boot application and need a function that uses your specific data models.

Prompt:

"I'm building a movie recommendation system in a Spring Boot application. I have a Java POJO class called 'Movie':

public class Movie {
    private String title;
    private String genre;
    private double rating;
    // Getters and setters omitted
    
    public String getGenre() {
        return this.genre;
    }
}

Now, write a Java method that takes a List<Movie> and a String genre, and returns a new list containing only the movies that match that genre."

Result (from the AI):

Java

import java.util.List;
import java.util.stream.Collectors;

public class MovieService {
    public List<Movie> filterMoviesByGenre(List<Movie> movies, String genre) {
        if (movies == null || genre == null) {
            return List.of(); // Return an empty list
        }

        return movies.stream()
                .filter(movie -> genre.equalsIgnoreCase(movie.getGenre()))
                .collect(Collectors.toList());
    }
}

Note: Because you provided the Movie class context, the AI knew to use movie.getGenre() and even used modern Java (Streams) to solve it.

6. Prompt Chaining: The "One Step at a Time" Command#

What is it? Prompt Chaining is the technique of breaking a single, massive task into a series of smaller, connected prompts. The output of one prompt becomes the input or context for the next.

Why It’s Important: This is the ultimate technique for complex workflows. An AI can get overwhelmed or "forget" instructions on a very large task. Chaining allows you to guide it step-by-step, review each part, and build a high-quality final product. It's how you "collaborate" with the AI.

Real-World Java Example: You want to build a new feature, from the data model all the way to the service logic.

Prompt 1:"Create a simple Java class for a 'Product' in an e-commerce system. It should include fields for id, name, and price."

(AI generates the Product class)

Prompt 2 (in the same chat):"Great. Now, using the 'Product' class you just created, write a Java method for a 'ProductRepository' class that finds the most expensive product from a List<Product>."

Result (from AI for Prompt 2):

Java

import java.util.List;
import java.util.Comparator;
import java.util.Optional;

public class ProductRepository {
    public Optional<Product> findMostExpensiveProduct(List<Product> products) {
        if (products == null || products.isEmpty()) {
            return Optional.empty();
        }

        return products.stream()
                .max(Comparator.comparingDouble(Product::getPrice));
    }
}

What's Next?#

You now have 6 powerful prompt engineering techniques in your toolkit. The best way to learn is to practice. Open your IDE or a chat window and try them out! You'll be surprised how much your AI interactions improve.

In Part 2 of this series, we'll explore even more advanced techniques, including Role-based Prompting, Task-Based Prompting and more.