AI-Driven Development: Exploring Bespoke Software Generation with LLMs and Restricted Agents
In a recent VentureBeat article, Anthropic co-founder Dario Amodei painted a compelling picture of AI’s future in software development. https://venturebeat.com/ai/bespoke-software-on-demand-anthropics-ai-powered-future/ He envisioned AI systems that don’t just use pre-built software but craft bespoke solutions on demand, collaborating with humans to build the exact software needed for a given situation. Inspired by this concept, I’ve embarked on an exploration of these possibilities, focusing on creating an online store as a proof of concept.
Setting the Scene: The Online Store Project
For this exploration, I’ve chosen to develop an online store application using a specific tech stack: ASP.NET Core for the backend and React for the frontend. The project leverages an opinionated development approach, incorporating specific libraries and architectural patterns:
Additionally, the project utilizes a library of pre-built components and a solution template that serves as a foundation for the generated application.
Introducing Restricted Agents
At the core of this exploration are “restricted agents” - specialized AI systems deliberately constrained in their capabilities, knowledge, or actions. Unlike general AI agents, these are designed for specific tasks or domains, often with built-in limitations.
In the context of the web development project, a restricted agent is an AI assistant tailored specifically for ASP.NET Core and React development. It possesses deep knowledge of these technologies but limited understanding of other programming languages or frameworks. This specialization allows it to provide more accurate and relevant assistance for the specific project needs.
Key characteristics of the restricted agents include:
Using restricted agents helps maintain focus, ensures relevant advice, and potentially reduces security risks compared to more general AI assistants.
The Experimental Setup
For this proof of concept, I’ve created a C# console application to experiment with AI-driven development. The system utilizes Claude 3.5 Sonnet, Anthropic’s newest and most powerful language model, as the core AI engine.
Since Anthropic doesn’t provide an official .NET SDK, I’ve implemented a custom solution to interact with their API. Here’s a snippet of the core functionality:
private async Task CallAnthropicAPIAsync(string system, IList<Message> messages, int maxTokens, Action<string> onTextDelta)
{
string apiUrl = "https://api.anthropic.com/v1/messages";
using (var client = new HttpClient())
{
client.Timeout = TimeSpan.FromMinutes(5);
client.DefaultRequestHeaders.Add("anthropic-version", "2023-06-01");
client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));
client.DefaultRequestHeaders.Add("x-api-key", apiKey);
var requestData = new
{
model = "claude-3-5-sonnet-20240620",
messages = messages,
max_tokens = maxTokens,
stream = true,
system = system
};
var json = JsonSerializer.Serialize(requestData, new JsonSerializerOptions
{
DefaultIgnoreCondition = System.Text.Json.Serialization.JsonIgnoreCondition.WhenWritingNull
});
var content = new StringContent(json, Encoding.UTF8, "application/json");
using (var response = await client.PostAsync(apiUrl, content))
{
try
{
response.EnsureSuccessStatusCode();
}
catch (HttpRequestException ex)
{
Console.WriteLine($"Request failed with status code: {(int)response.StatusCode}");
Console.WriteLine($"Reason phrase: {response.ReasonPhrase}");
Console.WriteLine($"Request URI: {response.RequestMessage.RequestUri}");
Console.WriteLine($"Request method: {response.RequestMessage.Method}");
Console.WriteLine($"Request headers:");
foreach (var header in response.RequestMessage.Headers)
{
Console.WriteLine($" {header.Key}: {header.Value}");
}
Console.WriteLine($"Request content:");
Console.WriteLine(await response.RequestMessage.Content.ReadAsStringAsync());
Console.WriteLine($"Response headers:");
foreach (var header in response.Headers)
{
Console.WriteLine($" {header.Key}: {header.Value}");
}
Console.WriteLine($"Response content:");
Console.WriteLine(await response.Content.ReadAsStringAsync());
throw ex;
}
using (var stream = await response.Content.ReadAsStreamAsync())
using (var reader = new StreamReader(stream))
{
while (!reader.EndOfStream)
{
var line = await reader.ReadLineAsync();
if (line.StartsWith("data:"))
{
var eventData = line.Substring("data:".Length).Trim();
if (eventData.StartsWith("{\"type\":\"content_block_delta\""))
{
var contentBlockDelta = JsonSerializer.Deserialize<ContentBlockDelta>(eventData);
if (contentBlockDelta.delta.type == "text_delta")
{
onTextDelta(contentBlockDelta.delta.text);
}
}
}
}
}
}
}
}
public class Message
{
public string role { get; set; }
public List<Content> content { get; set; }
}
public class Content
{
public string type { get; set; }
public string? text { get; set; }
public ImageSource? source { get; set; }
}
public class ImageSource
{
public string type { get; set; }
public string media_type { get; set; }
public string data { get; set; }
}
public class ContentBlockDelta
{
public Delta delta { get; set; }
}
public class Delta
{
public string type { get; set; }
public string text { get; set; }
}
This implementation allows for seamless communication with Claude, enabling me to send prompts and receive responses in a streaming fashion.
The AI-Driven Development Workflow
This is a proposed AI-driven development workflow which consists of several specialized agents, each responsible for a specific aspect of the application development process. A key feature of this workflow is the efficient handoff of tasks between agents, which allows for more focused and effective use of the AI’s capabilities.
Task Handoff Process
After each agent completes its task, it doesn’t pass the entire project context to the next agent. Instead, it distills the relevant information and passes only what’s necessary for the next stage. This approach has several benefits:
Here’s an example of how this handoff process works in practice:
<EntityDefinitions>
<Entity name="Product">
<Properties>
<Property name="Name" type="string" />
<Property name="Price" type="decimal" />
<Property name="Description" type="string" />
</Properties>
</Entity>
<Entity name="Order">
<Properties>
<Property name="OrderDate" type="DateTime" />
<Property name="TotalAmount" type="decimal" />
</Properties>
<Relationships>
<Relationship type="HasMany" with="Product" />
</Relationships>
</Entity>
</EntityDefinitions>
<EndpointRequirements>
<Entity name="Product">
<Operations>
<Operation type="GetAll" />
<Operation type="GetById" />
<Operation type="Create" />
<Operation type="Update" />
</Operations>
</Entity>
<Entity name="Order">
<Operations>
<Operation type="Create" />
<Operation type="GetByCustomerId" />
</Operations>
</Entity>
</EndpointRequirements>
<EndpointDefinition>
<Path>/api/products</Path>
<Method>GET</Method>
<QueryParameters>
<Parameter name="category" type="string" optional="true" />
<Parameter name="page" type="int" optional="true" />
</QueryParameters>
<ResponseType>List<ProductDto></ResponseType>
</EndpointDefinition>
This process continues through the component creation stages, with each handoff providing just the necessary information for the next task.
By implementing this focused handoff process, we enable each agent to work more efficiently and effectively, producing higher quality outputs for each specific task in the development process.
For the purposes of my POC I have implemented a simple analysis agent, components and component agent. I will provide example prompts and example output later in this article.
Let’s dive deeper into each agent’s role, with examples of their inputs and outputs:
1. Analysis Agent
The Analysis Agent is the first point of contact in the AI-driven development process. Its primary role is to engage in a comprehensive conversation with the user about the application and its requirements.
Purpose: To gather and clarify all necessary information about the desired application.
Process:
Output Example:
<ApplicationRequirements>
<Overview>
<Purpose>Online store for handmade crafts</Purpose>
<TargetAudience>Craft enthusiasts and gift shoppers</TargetAudience>
</Overview>
<Features>
<Feature>
<Name>Product Catalog</Name>
<Description>Browsable list of craft items with details and images</Description>
</Feature>
<Feature>
<Name>Shopping Cart</Name>
<Description>Ability to add items and proceed to checkout</Description>
</Feature>
<Feature>
<Name>User Reviews</Name>
<Description>Customers can leave reviews and ratings for products</Description>
</Feature>
</Features>
<TechnicalRequirements>
<Requirement>Responsive design for mobile and desktop</Requirement>
<Requirement>Secure payment processing</Requirement>
<Requirement>Integration with inventory management system</Requirement>
</TechnicalRequirements>
</ApplicationRequirements>
This structured output serves as the foundation for all subsequent stages of the development process.
2. Domain Agent
The Domain Agent takes the requirements gathered by the Analysis Agent and translates them into a concrete domain model.
Purpose: To define the core domain models and business logic of the application.
Process:
Output Example:
<DomainModel>
<Entity name="Product">
<Properties>
<Property name="Id" type="Guid" />
<Property name="Name" type="string" />
<Property name="Description" type="string" />
<Property name="Price" type="decimal" />
<Property name="StockQuantity" type="int" />
</Properties>
<Methods>
<Method name="DecreaseStock">
<Parameter name="quantity" type="int" />
</Method>
</Methods>
</Entity>
<Entity name="Order">
<Properties>
<Property name="Id" type="Guid" />
<Property name="CustomerId" type="Guid" />
<Property name="OrderDate" type="DateTime" />
<Property name="TotalAmount" type="decimal" />
</Properties>
<Relationships>
<Relationship type="OneToMany" with="OrderItem" />
</Relationships>
</Entity>
<!-- More entities as needed -->
</DomainModel>
This domain model serves as the blueprint for the application’s data structure and business logic.
3. Endpoints Agent
The Endpoints Agent designs the API structure based on the domain model and application requirements.
Purpose: To define the necessary API endpoints for the application.
Process:
Output Example:
<APIEndpoints>
<Endpoint path="/api/products" method="GET">
<Description>Retrieve list of products</Description>
<QueryParameters>
<Parameter name="category" type="string" optional="true" />
<Parameter name="page" type="int" optional="true" />
</QueryParameters>
<Response>
<Type>Array of Product</Type>
</Response>
</Endpoint>
<Endpoint path="/api/orders" method="POST">
<Description>Create a new order</Description>
<RequestBody>
<Property name="customerId" type="Guid" />
<Property name="items" type="Array of OrderItem" />
</RequestBody>
<Response>
<Type>Order</Type>
</Response>
</Endpoint>
<!-- More endpoints as needed -->
</APIEndpoints>
This API design guides the implementation of both backend endpoints and frontend API interactions.
4. API Agents
The API Agents are a collection of specialized agents that handle various aspects of API implementation.
4.1 Command/Query Creator
Purpose: To create CQRS Commands and Queries based on the API design.
Process:
Output Example:
<CQRSDefinitions>
<Command name="CreateOrderCommand">
<Properties>
<Property name="CustomerId" type="Guid" />
<Property name="Items" type="List<OrderItemDto>" />
</Properties>
</Command>
<Query name="GetProductsQuery">
<Properties>
<Property name="Category" type="string" optional="true" />
<Property name="Page" type="int" optional="true" />
</Properties>
<ReturnType>List<ProductDto></ReturnType>
</Query>
</CQRSDefinitions>
4.2 Endpoint Code Generator
Purpose: To generate the actual endpoint code in Program.cs.
Process:
Output Example:
app.MapPost("/api/orders", async (CreateOrderCommand command, IMediator mediator) =>
{
var result = await mediator.Send(command);
return Results.Created($"/api/orders/{result.Id}", result);
});
app.MapGet("/api/products", async (string? category, int? page, IMediator mediator) =>
{
var query = new GetProductsQuery { Category = category, Page = page };
var result = await mediator.Send(query);
return Results.Ok(result);
});
4.3 API Hook Creator
Purpose: To develop API hooks for frontend integration.
Process:
Output Example:
import { useQuery, useMutation } from 'react-query';
export const useGetProducts = (category, page) => {
return useQuery(['products', category, page],
() => fetchProducts(category, page));
};
export const useCreateOrder = () => {
return useMutation(createOrder);
};
5. Components Agent
The Components Agent oversees the creation of React components for the frontend.
Purpose: To design the overall component structure of the application.
Process:
Output Example:
<ComponentStructure>
<Component name="ProductList">
<Description>Displays a grid of product cards</Description>
<Props>
<Prop name="category" type="string" optional="true" />
</Props>
<Children>
<Child name="ProductCard" />
<Child name="Pagination" />
</Children>
</Component>
<Component name="ShoppingCart">
<Description>Shows items in cart and checkout button</Description>
<State>
<Item name="cartItems" type="Array of CartItem" />
</State>
<Children>
<Child name="CartItem" />
<Child name="CheckoutButton" />
</Children>
</Component>
<!-- More components as needed -->
</ComponentStructure>
6. Component Agent
The Component Agent focuses on the implementation of individual React components.
Purpose: To create detailed React components based on the component structure.
Process:
Output Example:
import React from 'react';
import { useGetProducts } from '../hooks/api';
import ProductCard from './ProductCard';
import Pagination from './Pagination';
const ProductList = ({ category }) => {
const [page, setPage] = React.useState(1);
const { data, isLoading, error } = useGetProducts(category, page);
if (isLoading) return <div>Loading...</div>;
if (error) return <div>Error: {error.message}</div>;
return (
<div className="container mx-auto">
<div className="grid grid-cols-1 md:grid-cols-3 gap-4">
{data.products.map(product => (
<ProductCard key={product.id} product={product} />
))}
</div>
<Pagination
currentPage={page}
totalPages={data.totalPages}
onPageChange={setPage}
/>
</div>
);
};
export default ProductList;
Each of these agents is designed to output both explanatory text and code. To facilitate this, I’ve implemented a utility function that extracts code blocks from the AI’s responses:
public static string ExtractCodeBlockOrKeepOriginal(string content)
{
var lines = content.Split(new[] { "\r\n", "\r", "\n" }, StringSplitOptions.None);
var inCodeBlock = false;
var extractedContent = new StringBuilder();
foreach (var line in lines)
{
if (line.TrimStart().StartsWith("```"))
{
if (inCodeBlock)
{
// End of code block
break;
}
else
{
// Start of code block
inCodeBlock = true;
continue;
}
}
if (inCodeBlock)
{
extractedContent.AppendLine(line);
}
}
// If we found a code block, use the extracted content; otherwise, return the original
return extractedContent.Length > 0 ? extractedContent.ToString().TrimEnd() : content;
}
This approach allows us to leverage the LLM’s reasoning capabilities and chain-of-thought processes to improve the quality of the generated code.
Prompt Examples: System Prompt After Clarification
In the AI-driven development process, we use a series of prompts to guide the AI in generating the required components. One of the most crucial prompts is the system prompt that we use after the initial clarification phase. This prompt sets the stage for the AI to analyze the application requirements and generate a structured plan for the components.
Here’s an example of the system prompt after clarification (including placeholders for information which is injected into the prompt):
# LLM React Component Generator
## Information Gathering and Clarification
You are an AI assistant tasked with analyzing information for a React application and asking clarifying questions if necessary. Your goal is to gather enough information to later generate React components. Follow these steps:
1. Carefully review the following information:
Application Description:
{applicationDescription}
Clarifications regarding application behaviours and requirements:
{clarifications}
Available npm Packages:
{npmPackages}
Available API Hooks:
{apiHooks}
Existing Directory Structure:
{directoryTree}
Key libraries: react-query, reactstrap, bootstrap
2. Authentication Information:
{auth}
3. Analyze the provided API methods to determine the required pages and behaviors of the application. Make informed assumptions about the expected functionality based on common patterns in web applications.
4. Attempt to infer as much as possible about the required behavior and user interactions. Use your knowledge of best practices in web application design to fill in any gaps.
5. After your analysis, provide a brief summary of your understanding of the application, including:
- The main purpose and features of the application
- Key components you expect to create
- Any assumptions you've made about functionality or user flow
Then based on the gathered information, inferences, and any necessary clarifications, your task is to plan out the necessary components for the application. Output this information in a structured XML format.
This prompt is designed to guide the AI through a comprehensive analysis of the application requirements. It asks the AI to review the application description, available packages and API hooks, and the existing directory structure. The AI is then instructed to make informed assumptions about the application’s functionality and user interactions based on common web application patterns and best practices.
The prompt also provides an example of the expected XML output format:
<components>
<component>
<name>UserProfile</name>
<path>./src/components/users/UserProfile.tsx</path>
<props>
<prop>userId: string</prop>
<prop>isEditable: boolean</prop>
</props>
<apiHooks>
<hook>useGetUserProfile</hook>
<hook>useUpdateUserProfile</hook>
</apiHooks>
<description>
Displays and allows editing of user profile information.
Shows user's handle, bio, and list of items for sale.
Inferred behavior: Allows users to update their profile picture and change their handle.
Uses authentication to ensure only the logged-in user can edit their own profile.
</description>
</component>
<!-- Add more components as needed -->
</components>
This structured output allows us to easily parse and use the AI’s analysis in subsequent steps of our development process. The XML format provides a clear overview of each component, including its name, file path, props, API hooks, and a description of its purpose and behavior.
By using this system prompt, we ensure that the AI thoroughly analyzes the application requirements and provides a comprehensive plan for the components needed. This approach allows us to leverage the AI’s capabilities in understanding complex application structures and inferring necessary functionalities, ultimately speeding up our development process and ensuring consistency across our application architecture.
Actual Results: Full AI-Generated Components
To demonstrate the effectiveness of our AI-driven development approach, let’s examine two components generated by our system in their entirety: the AdminDashboard and CurrencyDisplay components. We’ll also look at the XML structure that defines our components.
XML Component Structure
Our AI system generates an XML structure that defines each component, including its name, file path, props, API hooks, and a brief description. Here’s an example of this structure:
<components>
<component>
<name>Header</name>
<path>./src/components/layout/Header.tsx</path>
<props></props>
<apiHooks>
</apiHooks>
<description>
Main navigation component. Includes logo, search bar, and user menu.
Adapts based on user authentication status and role.
</description>
</component>
<component>
<name>Footer</name>
<path>./src/components/layout/Footer.tsx</path>
<props></props>
<apiHooks></apiHooks>
<description>
Footer component with links to important pages and copyright information.
</description>
</component>
<!-- More components... -->
</components>
Example 1: AdminDashboard Component (Full Code)
Here’s the complete AdminDashboard component generated by our AI system:
import React, { useState } from 'react';
import { useGetPagedUsers, useGetPagedStores, useGetReport, useAuthentication } from '../../hooks/api';
import { Container, Row, Col, Nav, NavItem, NavLink, TabContent, TabPane, Table, Badge, Spinner } from 'reactstrap';
import { Pagination } from '../common/Pagination';
import { ErrorAlert } from '../common/ErrorAlert';
import { FontAwesomeIcon } from '@fortawesome/react-fontawesome';
import { faUser, faStore, faChartBar } from '@fortawesome/free-solid-svg-icons';
import { Navigate } from 'react-router-dom';
const AdminDashboard: React.FC = () => {
const [activeTab, setActiveTab] = useState('1');
const [userPage, setUserPage] = useState(1);
const [storePage, setStorePage] = useState(1);
const { isSysAdmin } = useAuthentication();
const { data: users, isLoading: usersLoading, error: usersError } = useGetPagedUsers({ page: userPage, pageSize: 10 });
const { data: stores, isLoading: storesLoading, error: storesError } = useGetPagedStores({ page: storePage, pageSize: 10 });
const { data: report, isLoading: reportLoading, error: reportError } = useGetReport();
// Redirect if not a sysadmin
if (!isSysAdmin()) {
return <Navigate to="/" replace />;
}
const renderUserTable = () => (
<Table striped responsive>
<thead>
<tr>
<th>Email</th>
<th>Handle</th>
<th>Name</th>
<th>Status</th>
<th>Roles</th>
</tr>
</thead>
<tbody>
{users?.items.map(user => (
<tr key={user.id}>
<td>{user.email}</td>
<td>{user.handle}</td>
<td>{`${user.firstName} ${user.lastName}`}</td>
<td><Badge color={user.status === 'Active' ? 'success' : 'danger'}>{user.status}</Badge></td>
<td>{user.roles.join(', ')}</td>
</tr>
))}
</tbody>
</Table>
);
const renderStoreTable = () => (
<Table striped responsive>
<thead>
<tr>
<th>Name</th>
<th>Handle</th>
<th>Admins Count</th>
<th>Status</th>
</tr>
</thead>
<tbody>
{stores?.items.map(store => (
<tr key={store.id}>
<td>{store.name}</td>
<td>{store.handle}</td>
<td>{store.adminsCount}</td>
<td><Badge color={store.status === 'Active' ? 'success' : 'danger'}>{store.status}</Badge></td>
</tr>
))}
</tbody>
</Table>
);
const renderReports = () => (
<div>
{report?.map((resultSet, index) => (
<div key={index}>
<h4>{resultSet.name}</h4>
<Table striped responsive>
<thead>
<tr>
{resultSet.columns.map(column => (
<th key={column}>{column}</th>
))}
</tr>
</thead>
<tbody>
{resultSet.rows.map((row, rowIndex) => (
<tr key={rowIndex}>
{row.map((cell, cellIndex) => (
<td key={cellIndex}>{cell}</td>
))}
</tr>
))}
</tbody>
</Table>
</div>
))}
</div>
);
return (
<Container className="mt-4">
<h1 className="mb-4">Admin Dashboard</h1>
<Nav tabs>
<NavItem>
<NavLink
className={activeTab === '1' ? 'active' : ''}
onClick={() => setActiveTab('1')}
>
<FontAwesomeIcon icon={faUser} className="me-2" />
Users
</NavLink>
</NavItem>
<NavItem>
<NavLink
className={activeTab === '2' ? 'active' : ''}
onClick={() => setActiveTab('2')}
>
<FontAwesomeIcon icon={faStore} className="me-2" />
Stores
</NavLink>
</NavItem>
<NavItem>
<NavLink
className={activeTab === '3' ? 'active' : ''}
onClick={() => setActiveTab('3')}
>
<FontAwesomeIcon icon={faChartBar} className="me-2" />
Reports
</NavLink>
</NavItem>
</Nav>
<TabContent activeTab={activeTab}>
<TabPane tabId="1">
<Row className="mt-3">
<Col>
{usersLoading && <Spinner color="primary" />}
{usersError && <ErrorAlert error={usersError} />}
{users && (
<>
{renderUserTable()}
<Pagination
currentPage={users.page}
totalPages={users.totalPages}
onPageChange={setUserPage}
/>
</>
)}
</Col>
</Row>
</TabPane>
<TabPane tabId="2">
<Row className="mt-3">
<Col>
{storesLoading && <Spinner color="primary" />}
{storesError && <ErrorAlert error={storesError} />}
{stores && (
<>
{renderStoreTable()}
<Pagination
currentPage={stores.page}
totalPages={stores.totalPages}
onPageChange={setStorePage}
/>
</>
)}
</Col>
</Row>
</TabPane>
<TabPane tabId="3">
<Row className="mt-3">
<Col>
{reportLoading && <Spinner color="primary" />}
{reportError && <ErrorAlert error={reportError} />}
{report && renderReports()}
</Col>
</Row>
</TabPane>
</TabContent>
</Container>
);
};
export default AdminDashboard;
Example 2: CurrencyDisplay Component (Full Code)
Here’s the complete CurrencyDisplay component generated by our AI system:
import React from 'react';
import { getSymbolFromCurrency } from 'currency-symbol-map';
import numeral from 'numeral';
interface CurrencyDisplayProps {
val: number | null | undefined;
currency?: string | null;
}
const CurrencyDisplay: React.FC<CurrencyDisplayProps> = ({ val, currency = 'USD' }) => {
// If val is null or undefined, return a dash
if (val == null) {
return <span>-</span>;
}
// Get the currency symbol based on the currency code
const symbol = getSymbolFromCurrency(currency || 'USD') || '$';
// Format the number with 2 decimal places
const formattedValue = numeral(val).format('0,0.00');
return (
<span>
{symbol}{formattedValue}
</span>
);
};
export default CurrencyDisplay;
These full code examples demonstrate the sophistication of our AI-driven development system. The AdminDashboard component showcases complex state management, data fetching, and UI composition, while the CurrencyDisplay component illustrates how the system handles simpler, reusable components with proper type checking and edge case handling.
Key points to note:
Conclusion and Future Directions
This exploration into AI-driven development with restricted agents represents a step towards the future envisioned by Anthropic’s co-founder. By creating a system of specialized AI agents, each focused on a specific aspect of web application development, we can move closer to the concept of on-demand bespoke software generation.
The use of Claude 3.5 Sonnet as the core AI engine, combined with custom API integration and specialized agents, provides a powerful framework for exploring the possibilities of AI-assisted software development. As we continue to refine this approach, we may see significant advancements in how software is conceptualized, designed, and implemented.
Future work could involve expanding the capabilities of our restricted agents, improving their coordination, and exploring ways to handle more complex software architectures. Additionally, integrating this approach with existing development workflows and tools could pave the way for more widespread adoption of AI-driven development practices.
As we venture into the AI-powered future of software development, it’s crucial to approach new technologies with a spirit of curiosity and experimentation. Small proof of concepts and exploratory projects, like the one described in this article, play a vital role in our journey towards harnessing AI’s full potential in software creation.
This approach aligns closely with the strategies outlined in my recent LinkedIn article, “Generative AI Adoption Strategy for CIOs” (https://www.dhirubhai.net/pulse/generative-ai-adoption-strategy-cios-jim-taylor-5r8bc/). In that piece, I emphasize that “Embracing a culture of experimentation is essential for driving innovation and discovering new opportunities for Generative AI adoption.” This project serves as a practical example of putting that principle into action.
These exploratory endeavors serve multiple important purposes:
The project described in this article is a tangible manifestation of the experimental culture advocated in my LinkedIn piece. It demonstrates how CIOs and technology leaders can move beyond theoretical discussions of AI adoption and into practical, hands-on exploration. This approach allows organizations to build confidence, develop expertise, and identify unique opportunities for AI integration in their specific contexts.