Build proof of concept in 5 minutes with Cody AI

Launching a brilliant project idea, only to discover later that it doesn't meet your users' needs or isn't feasible within your technical limits, can significantly slow progress. In software development, building, testing, and gathering feedback early on are crucial steps to avoid these pitfalls.

Cody is an AI coding assistant created by Sourcegraph to assist developers and engineering teams in understanding, writing, reviewing, and refactoring code, speeding up code generation and software development. This article will begin by discussing the key aspects of creating a proof of concept (PoC), including the best times to implement one, common use cases, and the benefits of incorporating a PoC into your project development workflow.

We'll also explore the next steps after successfully validating your project idea with a PoC and how to transition toward a minimum viable product (MVP).

After covering these foundational topics, the article will walk through how to leverage Cody to build a PoC quickly, showcasing its capabilities in planning, coding, and refining a book review app.

What is a proof of concept, and how does it work in software development?

A PoC is an important piece in the software development process and is often a pivotal part of business development. It involves building a simplified version of an application to verify the feasibility of a business idea from a technical and practical standpoint. The goal of a POC is to demonstrate whether the core concept can be realized using the available technology and resources.

By implementing a PoC, you can test essential functionalities, identify potential challenges, and collect user feedback to refine the project before moving forward with full-scale development. This feedback is vital in ensuring your product aligns with user needs, reducing risks, and informing future development decisions.

Key steps to creating a PoC for your project idea

Each step of creating a PoC is crucial for developers and project managers, who oversee the alignment of the project’s objectives and ensure that the PoC process meets both technical and business goals. Here’s a process to follow:

1. Define the project scope: Identify the core features and functionalities the PoC must demonstrate. This involves drafting a detailed requirements document and setting clear objectives for what the PoC should accomplish.
2. Plan the architecture: Sketch the system architecture, focusing on the elements needed to test the product idea. This may include the design of databases, APIs, and integrations with third-party services.
3. Set up the development environment: Prepare the technical environment necessary for development, including installing the required tools and libraries.
4. Develop the PoC: Start building the PoC and implement the core features identified earlier. This stage is about proving technical feasibility rather than delivering a polished product.
5. Test and refine: Once the PoC is built, test it thoroughly to identify any issues or potential improvements. Gather feedback from stakeholders to determine if the PoC meets the intended goals.

Project management tools can further streamline this process by helping track progress, manage timelines, and facilitate collaboration between the development team and stakeholders.

With a clear understanding of what a PoC is and how it can help validate a project idea, the next step is to set up the development environment to start building the book review app.

Setting up the development environment

For this PoC, Django will be used as the framework, and the development environment will be set up on Windows.

To build your PoC, you first need to set up the development environment.

While this tutorial will use Django, the methodology can be used across any framework.

Start by creating a new virtual environment env by running the command below:

    python -m venv env 

This command tells Python to create the virtual environment in a folder named env in the current directory.

On creation, the virtual environment is activated using the following:

    #windows
    env\Scripts\activate

    #linux
    source env/bin/activate

On activating the environment, you can install Django using the following command:

    pip install django

With your Django environment ready, the next step is to set up Cody. To do that, follow these steps:

• Sign up for a Sourcegraph account using a preferred authentication method—GitHub, GitLab, or Google.
  

• Install Cody for any of Sourcegraph's supported IDE - VS Code, IntelliJ (or any other JetBrains IDE), or Neovim.

NB: VS Code is the IDE used throughout this article.

Once Cody is installed and set up in your development environment, you can leverage its capabilities to plan and develop an application.

Planning the project with Cody

The plan is to build a book review app that allows users to add, view, and delete book reviews. Before going into the technical details, it's important to understand how leveraging an AI coding assistant like Cody for project planning can provide significant advantages and streamline development.

Advantages of using AI for project planning
One of the key challenges in software development is the initial planning phase, where decisions can significantly influence the project's success. Traditionally, project planning involves a lot of manual effort, from defining requirements to architecting the system. This process can be time-consuming, prone to oversight, and requires extensive expertise to cover all critical aspects.

Below are some of the advantages of using AI:

• Speed and efficiency: AI can quickly generate ideas, suggest features, and outline project structures based on best practices. What might take hours of brainstorming and research can be accomplished in minutes with AI assistance.
• Reduced cognitive load: Planning a project can be mentally draining, especially when juggling multiple aspects like functionality, architecture, and user experience. AI coding assistants like Cody can ease this burden by taking on the role of a brainstorming partner, providing options, and helping think through complex decisions.
• Iterative refinement: With AI, you can easily iterate on your plans. If initial ideas don't seem feasible or need adjustment, It can help refine them without starting from scratch. This flexibility allows for a more adaptive planning process responding to changing needs or insights.

Integrating AI into the project planning phase sets the stage for a more organized and efficient development process. With clear requirements, defined features, and a solid architecture, the subsequent coding and testing phases become much smoother. AI's ability to quickly adapt and provide real-time feedback ensures that your project remains aligned with its goals.

Now, let's use Cody’s chat feature to plan the book review app by assisting in defining the app's:

• requirements
• features
• architecture

Cody supports the latest LLMs, including Claude 3.5, GPT-4o, Gemini 1.5, and Mixtral-8x7B. Cody uses Claude 3.5 Sonnet by default, but you can upgrade to pro to access other models like GPT-4o and Gemini 1.5 Pro.
This tutorial uses GPT-4o.

Developing the app with Cody

Traditionally, developing a PoC involves several key steps that are undertaken manually:

1. Defining the project scope: The core features and functionalities that the PoC should demonstrate are identified, typically involving drafting a detailed requirements document.
2. Setting up the project structure: Next, the necessary directories and files for the project are created, version control like Git is set up, and folders are organized for code and documentation.
3. Writing initial code: The basic functionalities are coded, which usually includes setting up a web server, defining models, establishing the database, and writing functions to handle CRUD (Create, Read, Update, Delete) operations.
4. Testing: Finally, test cases are manually written and run to ensure that the basic functionalities work as expected, often involving unit tests for individual components and integration tests to verify how different parts of the application interact.

This approach, while effective, can be time-consuming and requires significant manual effort.

In comparison, an AI-assisted approach with tools like Cody simplifies the development process. AI significantly reduces the time needed to build and polish a PoC by automating routine tasks, generating foundational code, and offering real-time guidance.

Let’s begin the app development process by prompting Cody to set up a new Django project and app:

After setting up the project and app, prompt Cody to create the app’s views and templates:

Next, prompt Cody to suggest how to implement the search and filter functionality, as shown in the images below. While Cody provides the necessary steps and code snippets, you'll follow its instructions to create the files:

What’s a PoC app without basic styling? Cody can help with that, as illustrated in the images below:

Let’s test this out by starting the server with the following command:

    python manage.py runserver

Testing the PoC

While writing code is essential, ensuring that the code performs correctly under various conditions is equally important. Traditionally, the typical approach to testing a PoC involves:

• Manual test case writing: As developers, you manually write test cases for various application components, ensuring each function behaves as expected. This might include unit tests for individual functions, integration tests for combined modules, and functional tests to simulate user interactions.
• Debugging: When issues are identified during testing, the code is manually debugged, the source of errors tracked, and fixes applied. This often involves repetitive cycles of testing, identifying bugs, and refining the code.
• Refinement: Based on the outcomes of testing and debugging, the code is refined iteratively. This process continues until the application meets the desired performance and stability standards.

The traditional approach explained above relies heavily on the ability to anticipate potential issues, which can sometimes result in overlooked bugs or inefficiencies. However, AI can significantly enhance the testing and refinement process. For example, Cody can assist in the following ways:

• Test case generation: Cody can quickly generate test cases based on written code. Using Cody's command feature, simply highlight the specific code area you want to test; in this case, the Review model and Cody automatically generates the relevant unit tests.
  

• Refinement suggestions: Cody can continuously provide recommendations for optimizing and refining your code. When prompted, Cody will analyze our code and offer suggestions on ways to improve the book review app's performance, as demonstrated in the images below.

Based on the examples above, the AI-assisted approach to testing and refinement offers several key benefits. It speeds up the process by automating tasks like test case generation, leading to faster iterations. Additionally, it enhances efficiency by handling routine tasks, allowing us to focus on other aspects of the project.

What is the best time to implement a PoC?

The best time to implement a PoC is during the early stages of the software development process, especially when you're exploring a new product idea or tackling an unfamiliar technical challenge. A PoC is particularly useful when the feasibility of a solution is uncertain or when stakeholders need tangible proof that the concept can be realized before committing to full-scale development.

For startups and small teams, a PoC can also be a valuable tool for securing investors' support by demonstrating a product's viability. Additionally, enterprises can use PoCs to experiment with emerging technologies or new business models without fully integrating them into their existing systems.

Common use cases and concept examples for PoC

Now that you understand when to implement a PoC, it’s important to explore the scenarios where PoCs are most commonly used. Across various industries, PoCs provide clarity and reduce risks, particularly in high-innovation areas. Here are some typical use cases:

• Exploring new technologies: When adopting new frameworks, languages, or tools, a PoC can help determine whether they fit within your existing infrastructure.
• Testing market demand: Before developing a full product, companies may create a PoC to gauge interest and gather feedback from early adopters.
• Addressing technical uncertainties: If a particular feature or integration presents challenges, a PoC can help validate the feasibility of the solution.
• Experimenting with business models: Companies may use PoCs to test new business models or services before scaling them.

Benefits of PoC

With the various use cases in mind, it becomes clear why PoCs are so valuable. Implementing a PoC provides multiple benefits that can improve both the project outcome and the development process's efficiency. Some of the key benefits include:

• Risk reduction: By testing the feasibility of the product idea early on, you can identify potential technical hurdles and resolve them before investing significant resources.
• Early feedback: PoCs allow you to collect user feedback early in the development process from stakeholders and potential users. This feedback is invaluable for refining the product before committing to full development, ensuring that the final product aligns with user needs and expectations.
• Improved decision-making: With a PoC, you have a clearer understanding of the project's viability, making it easier to decide whether to move forward, pivot, or abandon the concept.
• Resource efficiency: PoC's also help ensure that you only commit resources to ideas that have been validated, reducing waste and optimizing development efforts.

Success criteria and next steps after a proof of concept

Once your PoC has demonstrated the feasibility of your product idea, the next step is moving toward developing a minimum viable product (MVP). This transition allows you to take the core functionalities proven in the PoC and expand them into a version of the product that real users can use and test. An MVP offers an excellent opportunity to gather more detailed feedback and make iterative improvements before scaling further.

However, if the PoC fails to meet expectations, don't see it as a setback. Instead, use the insights from the PoC to refine your approach, pivot to a different solution, or explore alternative directions.

Wrapping up

Traditional and AI-assisted approaches to developing a proof of concept each have unique advantages. The traditional method is well-established and provides full control over each step, making it a reliable choice for development teams that prefer hands-on coding. However, when speed, efficiency, and adaptability are key, an AI-assisted approach, like using Cody, offers significant benefits in addressing the needs of both your development team and your target market.

This tutorial demonstrated how Cody can build a PoC app quickly and efficiently, from planning to execution, allowing you to focus on creating solutions that resonate with your target audience. To experience how Cody can enhance your development workflow, sign up for free today and take your projects to the next level. Additionally, don’t miss the chance to connect with experienced developers, collaborate on exciting projects, and contribute to a growing community of innovators using Cody to build successful PoCs.

Resources

• Cody documentation
• How to write unit tests