Building Word Puzzle in Hours with Amazon Q

For the Amazon Q Build Games challenge, I decided to build "Wordnest," a classic word-finding game inspired by one of the mini games inside the game Bookworm Adventures. The goal was simple: create a retro-style game using Amazon's new AI coding companion. Here’s a compact look at how that journey unfolded.

Why Wordnest?

I chose a word game because it's a well-defined "classic" that presents interesting, yet solvable, programming challenges. It was the perfect candidate for testing an AI agent's ability to handle logic, data management, and front-end code, fitting the challenge theme of "Build Classics with Amazon Q" perfectly.

The AI-Powered Workflow: From Zero to Prototype

I'm utilizing Amazon Q as if they are a pair programmer. I quickly learned that effective prompting was key. Instead of vague requests, I used specific, action-oriented, and technical prompts.

Effective Prompting

• 
  Bad Prompt: "Make a word game."


• 
  Good Prompt: "Create web-game a word game to be hosted using static site hosting like Github Pages. The game will choose a random long word (between 8 to 12 letters) from given dictionary. The player has a time limit (30 seconds) to form as many valid English words as possible from that word’s letters. Words must be 4 or more letters long and must only constructable using letters of the source word. Show each character of the randomized word as clickable tile. Player can submit their words using either keyboard or clicking the tiles."

This specificity was crucial for tackling the game's core programming challenges.

AI Tackling Classic Challenges

The main hurdle in a word game is managing the game logic: generating the letter grid and validating user-submitted words.

1. Grid Generation: I asked Q to create a JavaScript function to pick a long word (between 8 to 12 letters) from a dictionary text file I provided. Each character of the randomized word should be shown as clickable tile. Q provided a solid starting point that I could then tweak for better gameplay balance.




2. Word Validation: This is where AI shined. The classic approach involves iterating through a massive word list. I prompted Q: "Write a JavaScript function that fetches a words.txt file and efficiently checks if a given word exists in the list."

Q returned a solution using a Set for near-instant lookups, a classic optimization that it suggested immediately. Below is the early version of the generated code before further optimization:

const potentialSourceWords = [];



// AI-Generated Code Snippet for Word Validation

async function loadWords() {

    const response = await fetch('words.txt');

    const text = await response.text();

    const allWords = text.split('\n');

    allWords.forEach(wordRaw => {

        const word = wordRaw.trim();

        if (word.length > 0) {

            dictionary.add(word); // Add every word to the main dictionary



            // If the word is between 8 and 12 letters, add it as a potential source word

            if (word.length >= 8 && word.length <= 12) {

                  potentialSourceWords.push(word);

            }

        }

    });

}



loadWords();



function validateWord() {

    const word = wordInput.value.toLowerCase();

    return potentialSourceWords.has(word);

}

This single piece of AI-generated code gave me a foundational piece of the game's logic to improve further.

Development Automation

Beyond core logic, Amazon Q accelerated the entire development process:

• 
  Boilerplate Code: It generated the initial index.html structure and style.css file in seconds.


• 
  Debugging: When my word submission logic failed, I fed the function to Q with the prompt, "Debug this function. It's not correctly adding letters from the randomized word." Q identified the error in my state management logic.


• 
  Feature Enchancement: I could ask Q to "add a button to share player's result to X" which was done in instant.

The Final Creation: Wordnest

The result is Wordnest, a fully functional word mini game. You can try Wordnest

!

Conclusion

Using Amazon Q for the Build Games challenge felt like having a tireless pair programmer at my side. It handled the boilerplate, suggested algorithms for classic problems, and helped me debug faster. This allowed me to focus less on routine coding and more on the creative aspects of game design and logic. For any developer looking to accelerate their workflow, integrating an AI assistant like Amazon Q is no longer a novelty; it's a massive productivity boost. This project also offered a glimpse into a future where the barrier between idea and execution is lower than ever. For solo developers, learners, and teams looking to prototype rapidly, tools like Amazon Q are game-changers. They don't replace developers; they amplify them.

#BuildGamesChallenge #AmazonQDevCLI