Board games have been a source of entertainment and intellectual challenge for centuries. With the advent of artificial intelligence, particularly ChatGPT, we now have the opportunity to explore new dimensions in game design and play. At the last OpenAI Application Explorers meetup Godfrey Nolan, President of RIIS, demonstrated how ChatGPT could be integrated into 4 classic board games, Chess, Scrabble, Wordle, and Sudoku. Follow this tutorial to find out how you can do the same and as usual, you can follow along with the recording from the meetup.
Creating Wordle with ChatGPT
Wordle, the popular word-guessing game, serves as an excellent starting point for our exploration of AI-assisted game creation. Let’s look at how we can use ChatGPT to generate a basic Wordle game using Flask.

To begin, we can prompt ChatGPT with:
This will generate a basic Flask application structure for our Wordle game. The key components of the Wordle game include:
A list of five-letter words
A function to generate feedback on guesses
Routes for handling game logic and user interactions
Here’s a simplified version of the Flask app for Wordle:
from flask import Flask, render_template, request, redirect, url_for, session
import random
app = Flask(__name__)
app.secret_key = "wordle_secret_key"
WORDS = ["apple", "grape", "peach", "melon", "mango", "berry", "lemon"]
def generate_feedback(secret_word, guess):
feedback = []
for i in range(len(guess)):
if guess[i] == secret_word[i]:
feedback.append("green")
elif guess[i] in secret_word:
feedback.append("yellow")
else:
feedback.append("gray")
return feedback
This code sets up the basic structure for our Wordle game. The generate_feedback
function is crucial as it determines whether guessed letters are correct and in the right position (green), correct but in the wrong position (yellow), or incorrect (gray).
@app.route("/", methods=["GET", "POST"])
def index():
if request.method == "POST":
guess = request.form["guess"].lower()
if "secret_word" not in session:
return redirect(url_for("reset"))
secret_word = session["secret_word"]
attempts = session.get("attempts", [])
if len(guess) != 5 or not guess.isalpha():
error = "Please enter a valid 5-letter word."
return render_template("index.html", attempts=session.get("attempts", []), error=error)
feedback = generate_feedback(secret_word, guess)
attempts.append({"guess": guess, "feedback": feedback}
session["attempts"] = attempts
if guess == secret_word:
return render_template("index.html", attempts=prepare_attempts(attempts), success=True, secret_word=secret_word)
elif len(attempts) == 6:
return render_template("index.html", attempts=prepare_attempts(attempts), failure=True, secret_word=secret_word)
return redirect(url_for("index"))
if "secret_word" not in session:
return redirect(url_for("reset"))
return render_template("index.html", attempts=prepare_attempts(session.get("attempts", [])))
This route handles the main game logic, processing user guesses and providing feedback.
@app.route("/reset")
def reset():
session["secret_word"] = random.choice(WORDS)
session["attempts"] = []
return redirect(url_for("index"))
def prepare_attempts(attempts):
return [zip(attempt["guess"], attempt["feedback"]) for attempt in attempts]
if __name__ == "__main__":
app.run(debug=True)
These functions handle resetting the game and preparing the attempts for display.
When building a Flask app, you need at minimum two components, the app running the Python code and an HTML template for the app to point to. The HTML should sit within a folder named templates
within your main folder. If you don’t get the associated HTML with your first prompt, ask GPT to generate HTML based on the code it originally supplied and you should get something like this:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Wordle</title>
<style>
body {
font-family: Arial, sans-serif;
text-align: center;
margin: 0;
padding: 20px;
}
.guess-container {
display: grid;
grid-template-columns: repeat(5, 50px);
gap: 10px;
justify-content: center;
margin: 20px 0;
}
.tile {
width: 50px;
height: 50px;
line-height: 50px;
font-size: 18px;
text-align: center;
border: 1px solid #ccc;
}
.green {
background-color: #6aaa64;
color: white;
}
.yellow {
background-color: #c9b458;
color: white;
}
.gray {
background-color: #787c7e;
color: white;
}
</style>
</head>
<body>
<h1>Wordle Game</h1>
{% if success %}
<h2>Congratulations! You guessed the word: {{ secret_word }}</h2>
<a href="{{ url_for('reset') }}">Play Again</a>
{% elif failure %}
<h2>Game Over! The word was: {{ secret_word }}</h2>
<a href="{{ url_for('reset') }}">Try Again</a>
{% else %}
<form method="post" action="{{ url_for('index') }}">
<input type="text" name="guess" maxlength="5" required>
<button type="submit">Submit Guess</button>
</form>
{% if error %}
<p style="color: red;">{{ error }}</p>
{% endif %}
{% endif %}
<div>
{% for attempt in attempts %}
<div class="guess-container">
{% for letter, color in attempt %}
<div class="tile {{ color }}">{{ letter }}</div>
{% endfor %}
</div>
{% endfor %}
</div>
</body>
</html>
This HTML template creates a simple but functional interface for our Wordle-like game, complete with a grid to display guesses and color-coded feedback. Since this is a simple app, we’ve embedded the styles rather than creating a separate CSS file. You may notice the conditional {} sets. These tie back to Boolean variables we set up within the if guess == secret_word:
of our app.py
file.
To expand our word list, we can ask ChatGPT to generate more words:
"generate 100 more 5 letter real words similar to the list WORDS = ["apple", "grape", "peach", "melon", "mango", "berry", "lemon"]"
This will provide us with a larger pool of words, making the game more diverse and challenging.
To test your Wordle game run python app.py
in the console and you should get a local address to test it on. Open it up and the browser and here’s what you should see:

Submit your guess and start playing!

Wordle is almost a perfect game for an LLM to create since the rules are simple, but what about something a little bit more conceptual?
Sudoku: A Numbers Game
Moving from words to numbers, let’s explore how ChatGPT can help create a Sudoku game. Sudoku is a logic-based number-placement puzzle where the objective is to fill a 9×9 grid with digits so that each column, row, and 3×3 sub-grid contains all digits from 1 to 9.

To start building our Sudoku game, we can prompt ChatGPT with:
This will give us a foundation for creating Sudoku puzzles that we can test and then build on. The first part of the Flask app should look like this.
from flask import Flask, render_template, request, redirect, url_for, flash
app = Flask(__name__)
app.secret_key = "secret_key"
initial_grid = [
[5, 3, 0],
[6, 0, 0],
[0, 9, 8],
]
That’s our starting grid, with blank cells for the user to fill in represented by 0.
To ensure that our LLM hasn’t hallucinated, you need to make sure in the next blocks that these key components of a Sudoku game are included:
A function to generate valid Sudoku puzzles
A mechanism to check the validity of moves
A user interface to display the puzzle and accept inputs
Here’s a basic structure for a Sudoku puzzle generator:
def is_valid_grid(grid):
"""
Validate the 3x3 Sudoku grid.
"""
for row in grid:
if not is_valid_unit(row):
return False
for col in range(3):
column = [grid[row][col] for row in range(3)]
if not is_valid_unit(column):
return False
return True
def is_valid_unit(unit):
"""
Check if a unit (row or column) contains unique non-zero values.
"""
non_zero_values = [num for num in unit if num != 0]
return len(non_zero_values) == len(set(non_zero_values))
These functions handle our validation and individual grid units, but since this a Flask app we will also need to define our routes and logic for interacting with the HTML.
@app.route("/", methods=["GET", "POST"])
def sudoku():
if request.method == "POST":
try:
# Get the grid values from the form
grid = []
for i in range(3):
row = []
for j in range(3):
value = request.form.get(f"cell-{i}-{j}")
row.append(int(value) if value.isdigit() else 0)
grid.append(row)
# Validate the grid
if is_valid_grid(grid):
flash("The grid is valid!", "success")
else:
flash("The grid is invalid!", "danger")
except Exception as e:
flash(f"Error: {e}", "danger")
return redirect(url_for("sudoku"))
return render_template("index.html", grid=initial_grid)
if __name__ == "__main__":
app.run(debug=True)
This structure provides a foundation for generating Sudoku puzzles and their grid structure, as well as a way for validating our grid after user input. Now we can move on to our HTML file.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>3x3 Sudoku</title>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.3.0/css/bootstrap.min.css">
</head>
<body>
<div class="container mt-4">
<h1 class="text-center">3x3 Sudoku</h1>
{% with messages = get_flashed_messages(with_categories=true) %}
{% if messages %}
<div>
{% for category, message in messages %}
<div class="alert alert-{{ category }}">{{ message }}</div>
{% endfor %}
</div>
{% endif %}
{% endwith %}
<form method="post">
<table class="table table-bordered text-center" style="width: 200px; margin: 0 auto;">
{% for i in range(grid|length) %}
<tr>
{% for j in range(grid[i]|length) %}
<td>
<input type="text"
name="cell-{{ i }}-{{ j }}"
value="{{ grid[i][j] if grid[i][j] != 0 else '' }}"
maxlength="1"
class="form-control text-center"
style="width: 50px;">
</td>
{% endfor %}
</tr>
{% endfor %}
</table>
<div class="text-center mt-3">
<button type="submit" class="btn btn-primary">Validate</button>
</div>
</form>
</div>
</body>
</html>
Most of this code is just building our table, but what is essential to our Sudoku game rests within the <input>
section inside our <form>
block. We essentially are making each cell a form field with a unique identifier. The values input here will interact with the logic in our backend to validate the grid. If all goes well, this is what you should see after running the app.

Great, now let’s fill in the numbers and give the validation button a spin.

A green box should pop up, letting you know the gird is valid!

Now that you have the working code, feel free to feed it back into ChatGPT with the following instruction:

This should be easy, but ChatGPT and many LLMs will struggle here. You may need to guide it through the way the grids need to be checked against each other. Remember, each needs to be self-consistent as well as correctly add with its neighbors. Think about how that would logically need to flow and instruct the LLM accordingly.
Chess: Strategy and AI
Chess, the ultimate game of strategy, presents an exciting challenge for AI integration. Instead of building from scratch this time, we are going to lean on available libraries to get started and we are then going to implement ChatGPT as our opponent using the FEN notation to guide its moves and translate the users.

First, we need to install our Chess library for Python.
This will bring in our FEN format for us. A clean board with all the pieces will look like this in FEN:
That looks intimidating, but each ‘/’ represents a row, uppercase letters are white, lowercase letters are black, and numbers are spaces. There are some additional notations, but this should be enough to have a base understanding.

Instead of using the latest and greatest model of ChatGPT, we are going to use the gpt-3.5-turbo-instruct model, which is better at Chess than the newer models. This is because 'instruct' had some human intervention in the training of the model and is the last model where that is the case.
Using our Chess library, we can feed it a FEN string like the one above and ask for it to return a set of all possible moves. We can then feed those moves to our LLM to help it make its move
Okay, knowing all this, let’s start building our flask app. Create our app.py
file and here’s what our first few lines should be:
from flask import Flask, request, jsonify, render_template
import chess
import openai
import os
app = Flask(__name__)
openai_api_key = os.getenv("OPENAI_API_KEY")
You will notice we are bringing in openai
, so if you haven’t installed that yet via pip
do so now. It is also recommended you put your API key in a config.py
file, but here we are setting it via the console as an environment variable. Next, we will set up our route.
@app.route('/')
def index():
return render_template('index.html')
This is a little smaller than usual because we will be leaning on a Javascript library to display our game board. Now let’s translate our FEN string into something ChatGPT can digest:
def fen_to_readable(fen):
board = chess.Board(fen)
readable_board = ""
for row in fen.split()[0].split('/'):
expanded_row = row.replace('1', ' ').replace('2', ' ').replace('3', ' ').replace('4', ' ').replace('5', ' ').replace('6', ' ').replace('7', ' ').replace('8', ' ')
readable_board += expanded_row + "\n"
turn = "White" if board.turn else "Black"
readable_board += f"Turn: {turn}\n"
return readable_board.strip()
Now we are going to use that to make a call to the OpenAI API:
def get_gpt_move(fen, rating, is_retry=False):
board = chess.Board(fen)
legal_moves = list(board.legal_moves)
legal_moves_uci = [move.uci() for move in legal_moves]
readable_board = fen_to_readable(fen)
print(f"Readable Board:\n{readable_board}")
retry_message = "The last move did not work. Please try again. " if is_retry else ""
prompt = f"""
{retry_message}You are playing chess at a skill level of {rating}. The current board position is as follows:
{readable_board}
These are the legal moves you can choose from: {', '.join(legal_moves_uci)}.
Please provide your next move in UCI format. Make a singular move.
"""
try:
print(f"Prompt to GPT: {prompt}")
response = openai.completions.create(
model="gpt-3.5-turbo-instruct",
prompt=prompt,
max_tokens=50,
temperature=0.5
)
gpt_move = response.choices[0].text.strip()
print(f"GPT suggested move: {gpt_move}")
return gpt_move
except Exception as e:
print(f"Error in get_gpt_move: {str(e)}")
raise
Here we are feeding the current state of the board to ChatGPT, validating the move and if it's incorrect, asking GPT to try again, telling ChatGPT it’s at a certain skill level, and then feeding it the legal moves that it can choose from. The output should be a legal move that we can then feed to our move process_move() which we are writing next.
@app.route('/move', methods=['POST'])
def move():
return process_move(is_retry=False)
@app.route('/retry_move', methods=['POST'])
def retry_move():
return process_move(is_retry=True)
def process_move(is_retry):
data = request.json
fen = data['fen']
rating = data['rating']
print(f"Received FEN: {fen}")
print(f"Received rating: {rating}")
try:
gpt_move = get_gpt_move(fen, rating, is_retry)
board = chess.Board(fen)
move = chess.Move.from_uci(gpt_move)
if move in board.legal_moves:
board.push(move)
new_fen = board.fen()
print(f"New FEN after move: {new_fen}")
return jsonify({"move": gpt_move})
else:
print(f"Invalid move generated: {gpt_move}")
return jsonify({"error": "Invalid move generated"}), 400
except Exception as e:
print(f"Error in move route: {str(e)}")
return jsonify({"error": str(e)}), 500
if __name__ == '__main__':
app.run(host='0.0.0.0', port=10000)
As per usual create an index.html file within a templates folder. Here our HTML is just some light scaffolding to call in our chessboard Javascript. We are leveraging Chris Oakman’s open source Javascript chessboard.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Play Chess with GPT</title>
<link rel="stylesheet" href="https://unpkg.com/@chrisoakman/chessboardjs@1.0.0/dist/chessboard-1.0.0.min.css">
<link rel="stylesheet" href="/static/frontend.css">
</head>
<body>
<h1>Play Chess with GPT</h1>
<div id="board" style="width: 400px;"></div>
<button id="reset">Reset Game</button>
<button id="retryMove" style="display: none;">Retry AI Move</button>
<div>
<label for="rating">Enter AI Rating (e.g., 800, 1200, 2000):</label>
<input type="number" id="rating" value="1200" min="100" max="3000">
</div>
<div id="status"></div>
<script src="https://code.jquery.com/jquery-3.5.1.min.js"></script>
<script src="https://unpkg.com/@chrisoakman/chessboardjs@1.0.0/dist/chessboard-1.0.0.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/chess.js/0.10.3/chess.min.js"></script>
<script src="/static/frontend.js"></script>
</body>
</html>
You will also need to download and place the static files within your app's directory. Run the app and you should see a fully functioning chess game like this:

In the console, you will be able to see the step-by-step processing of each move:

It’s kind of neat, but it still isn’t as good as a human player (yet).
Scrabble: Word Play and Strategy
Our last example almost combines everything we’ve done so far. The classic word game Scrabble features a grid with pieces, individual points per piece type, and a complex rule set that limits the way pieces can be ordered and placed. Let’s explore how we can create a Scrabble game using Flask and enhance it with AI-powered word suggestions.
To start building our Scrabble game, let’s move over to the Canvas UI and prompt ChatGPT with:

A board representation
A dictionary of valid words
A scoring system
Player management
AI opponent (optional)
You should receive a flask app that looks something like the following examples. Like before we need to set up our imports and main variables to get started in our app.py
file.
from flask import Flask, render_template, request, redirect, url_for, jsonify
import random
import openai
import config
app = Flask(__name__)
board = [["" for _ in range(15)] for _ in range(15)]
letter_bag = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ") * 2
random.shuffle(letter_bag)
players = {
"player1": [letter_bag.pop() for _ in range(7)],
"player2": [letter_bag.pop() for _ in range(7)]
}
openai.api_key = config.OPENAI_API_KEY
Here our board is a 15x15 grid, our letter bag contains the whole alphabet twice over, and we have two players.
Next, we will set up our main route and our place-word route as well:
@app.route("/")
def home():
return render_template("scrabble.html", board=board, players=players, points=None)
@app.route("/place_word", methods=["POST"])
def place_word():
global board
player = request.form["player"]
word = request.form["word"]
row = int(request.form["row"])
col = int(request.form["col"])
direction = request.form["direction"]
try:
if direction == "horizontal":
for i, char in enumerate(word):
if board[row][col + i] == "" or board[row][col + i] == char:
board[row][col + i] = char
else:
raise ValueError("Invalid move")
elif direction == "vertical":
for i, char in enumerate(word):
if board[row + i][col] == "" or board[row + i][col] == char:
board[row + i][col] = char
else:
raise ValueError("Invalid move")
for char in word:
if char in players[player]:
players[player].remove(char)
if letter_bag:
players[player].append(letter_bag.pop())
else:
raise ValueError("Player does not have the required letters")
except Exception as e:
return str(e), 400
return redirect(url_for("home"))
You can see that it’s pretty simple to restrict the word flow for either vertical or horizontal placement. It’s then just a matter of the user choosing the starting point and the letters from their chosen word can be filled in sequentially.
Lastly, we are going to leverage AI to create a hint system for us:
# Route to get possible words
@app.route("/get_possible_words", methods=["POST"])
def get_possible_words():
data = request.get_json()
letters = data.get("letters", "")
print(data)
response = openai.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "system",
"content": "you are a scrabble player given a series of letters come up with some words to play"
},
{
"role": "user",
"content": letters
}
],
max_tokens=100,
n=1,
)
possible_words = response.choices[0].message.content
print(response.choices[0].message.content)
return jsonify({"words": possible_words})
if __name__ == "__main__":
app.run(debug=True)
Our HTML template is going to be a lot more complex this time since, unlike the chess board, we are building this thing ourselves.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Scrabble Game</title>
<style>
body {
font-family: Arial, sans-serif;
margin: 20px;
padding: 0;
}
table {
border-collapse: collapse;
margin: 20px 0;
}
td {
width: 40px;
height: 40px;
border: 1px solid #000;
text-align: center;
vertical-align: middle;
font-size: 18px;
position: relative;
}
.double-word {
background-color: #FF9AA2;
}
.triple-word {
background-color: #FF6F61;
color: white;
}
.double-letter {
background-color: #B5EAD7;
}
.triple-letter {
background-color: #99C1F1;
color: white;
}
.center-star {
background-color: #FFD700;
}
.rack {
display: flex;
margin: 10px 0;
}
.rack span {
width: 40px;
height: 40px;
margin: 2px;
display: flex;
justify-content: center;
align-items: center;
border: 1px solid #000;
font-size: 18px;
cursor: grab;
}
.rack span.dragging {
opacity: 0.5;
}
form {
margin: 20px 0;
}
input[type="text"] {
margin: 5px;
padding: 5px;
}
button {
margin-top: 10px;
padding: 8px 15px;
}
.possible-words {
margin-top: 10px;
font-size: 16px;
color: #333;
}
</style>
</head>
<body>
<h1>Scrabble Game</h1>
<h2>Game Board</h2>
<table id="board">
{% for i in range(board|length) %}
<tr>
{% for j in range(board[i]|length) %}
{% set cell_class = '' %}
{% if (i == 7 and j == 7) %}
{% set cell_class = 'center-star' %}
{% elif (i == 0 and j == 0) or (i == 0 and j == 14) or (i == 14 and j == 0) or (i == 14 and j == 14) or (i == 7 and j == 7) %}
{% set cell_class = 'triple-word' %}
{% elif (i % 4 == 0 and j % 4 == 0) and i != 7 and j != 7 %}
{% set cell_class = 'double-word' %}
{% elif (i % 3 == 0 and j % 3 == 0) and (i != 0 and j != 0) %}
{% set cell_class = 'double-letter' %}
{% elif (i + j) % 8 == 0 %}
{% set cell_class = 'triple-letter' %}
{% endif %}
<td class="{{ cell_class }}" data-point="{{ points[i][j] if points else '' }}" data-row="{{ i }}" data-col="{{ j }}"></td>
{% endfor %}
</tr>
{% endfor %}
</table>
<h2>Player Racks</h2>
{% for player, rack in players.items() %}
<h3>{{ player }}</h3>
<div class="rack" id="{{ player }}-rack">
{% for letter in rack %}
<span draggable="true" data-letter="{{ letter }}">{{ letter }}</span>
{% endfor %}
</div>
<button onclick="getPossibleWords('{{ player }}')">Get Possible Words</button>
<div id="{{ player }}-words" class="possible-words"></div>
{% endfor %}
<h2>Place a Word</h2>
<form action="/place_word" method="POST">
<label for="player">Player:</label>
<select name="player" id="player">
{% for player in players.keys() %}
<option value="{{ player }}">{{ player }}</option>
{% endfor %}
</select>
<br>
<label for="word">Word:</label>
<input type="text" id="word" name="word" required>
<br>
<label for="row">Row:</label>
<input type="text" id="row" name="row" required>
<br>
<label for="col">Column:</label>
<input type="text" id="col" name="col" required>
<br>
<label for="direction">Direction:</label>
<select name="direction" id="direction">
<option value="horizontal">Horizontal</option>
<option value="vertical">Vertical</option>
</select>
<br>
<button type="submit">Place Word</button>
</form>
<script>
const tiles = document.querySelectorAll('.rack span');
const boardCells = document.querySelectorAll('#board td');
tiles.forEach(tile => {
tile.addEventListener('dragstart', (e) => {
tile.classList.add('dragging');
e.dataTransfer.setData('text/plain', tile.dataset.letter);
});
tile.addEventListener('dragend', () => {
tile.classList.remove('dragging');
});
});
boardCells.forEach(cell => {
cell.addEventListener('dragover', (e) => {
e.preventDefault();
});
cell.addEventListener('drop', (e) => {
e.preventDefault();
const letter = e.dataTransfer.getData('text/plain');
if (!cell.textContent.trim()) {
cell.textContent = letter;
}
});
});
function getPossibleWords(player) {
const rack = Array.from(document.querySelectorAll(`#${player}-rack span`)).map(el => el.textContent).join('');
fetch('/get_possible_words', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ letters: rack }),
})
.then(response => response.json())
.then(data => {
console.log('Response data:', data);
const wordsDiv = document.getElementById(`${player}-words`);
if (data.words) {
const words = data.words.split(',');
wordsDiv.textContent = `Possible words: ${words.join(', ')}`;
} else {
wordsDiv.textContent = 'No possible words found.';
}
})
.catch(err => {
console.error('Error fetching possible words:', err);
});
}
</script>
</body>
</html>
Okay, that’s a lot to digest, so let’s go part by part. The big chunk at the top is mostly our style content, which will be used to make the Scrabble board look Scrabble-y.
The <table id="board">
section creates a series of nested loops that assign our squares including the special squares for bonus points.
The <h2>Player Racks</h2>
creates our player racks and our awesome draggable letter pieces
The <h2>Place a Word</h2>
section contains a post action to connect to our backend route, our drag-and-drop functionality, and the call to our word suggestion feature.
We’re using embedded scripts to keep this all in one file, but if we were building an app any more complex than this, it would be worth separating our concerns and having individual CSS and Javascript files respectively.
If everything is in its right place, this is what you should see when you run the app:

Awesome, now give the word suggestion function a try by clicking the [Get Possible Words] button.

Congratulations, you’ve built a Scrabble coach that you can take anywhere.
Conclusion
The integration of AI, particularly ChatGPT, into board game development opens up exciting possibilities for both game designers and players. From generating word lists for Wordle to suggesting strategic moves in Chess, AI can enhance gameplay, provide intelligent opponents, and even serve as a learning tool.
Throughout this exploration, we’ve seen how AI can be applied to various aspects of game creation:
In Wordle, AI helps generate diverse word lists and can potentially assist in creating hints or difficulty levels.
For Sudoku, AI can generate puzzles of varying sizes and difficulties, pushing the boundaries of traditional gameplay.
In Scrabble, AI can suggest high-scoring words, helping players improve their vocabulary and strategic thinking.
For Chess, AI provides intelligent move suggestions and can offer explanations, serving as both an opponent and a tutor.
These applications demonstrate the versatility of AI in enhancing classic board games. As AI technology continues to advance, we can expect even more innovative integrations that will redefine how we create, play, and learn from board games. Now that you have the basic knowledge, try and enhance one of your own favorite games using ChatGPT!
Additional Resources
https://github.com/godfreynolan/wordle
https://github.com/godfreynolan/sudoku
https://github.com/godfreynolan/chess
https://github.com/godfreynolan/scrabble
https://chess-omx0.onrender.com/
https://www.linkedin.com/in/jason-loucks-200133272/
https://www.chessboardjs.com/
https://www.chess.com/terms/fen-chess
If you had fun with this tutorial, be sure to join the OpenAI Application Explorers Meetup Group to learn more about awesome apps you can build with AI