An End-to-End Guide to AI-Assisted Software Development

AI-assisted software development has already changed the way we create software. The 2024 Stack Overflow Developer Survey says that 76% of developers use AI-powered tools in their work. However, this is not the only use. AI also helps teams and big projects by cutting down on boring and repetitive tasks. So, adding AI to your processes speeds up your projects and makes them cheaper.

This material will help to leverage AI for software development. Let’s dive straight into the topic.

Planning, Designing, and Architecting

In this section of the tutorial, we’ll talk about how to use AI assisted software development to make your project planning easier and help you make better design choices, whether you’re developing a new app or redesigning an old one.

1. Prepare the Environment

Let’s set your workstation ready for an AI-assisted flow before we start working. First, you need to write down the prompts correctly. Since you’re going to test a lot of them, systematically retain the excellent ones so you can utilize them again later. Then, version control must be put in place. AI can make many things quickly and sometimes in a chaotic way; thus, use Git as a safety net.

Also, keep in mind that the quality of the output from artificial-intelligence software development tools changes according to how the prompt is formatted. For instance, using bullet points, let ChatGPT see each point as a unique, actionable command, which makes it more accurate and less likely to miss a detail. In fact, formatting matters less for specialized AI tools like GitHub Copilot. But that still makes things less clear. So, if you need to, change the structure of the samples below to fit your requirements.

2. Brief a Project

When using AI to create software, context is very important. So, give it the basics first. The key point is to provide AI with all the rules and limits, such as:

• Project Summary: Write a summary of the project you made and what it is for.
• Tech Stack: Write down the languages, frameworks, and tools you will employ.
• Core Features: List the most important features you wish to include.
• Limitations: Talk about any limits, such as the necessity for browser support or good performance.
• Code Style: Please let us know if you have any coding standards or architectural preferences that you would want to follow.

Using AI in software development, this prompt to start building your knowledge base:

I’m starting a new [type of project] using [programming language/framework]. Can you suggest a basic file structure and essential dependencies I should consider?

3. Set Milestones

Now let’s make a plan for the project. First, break the task up into smaller pieces that are easier to handle, like this:

Can you assist us in dividing this down into logical parts or modules based on our project overview? For each one, write down its name, what it does, the problems it has, and how it works with other modules. Also, recommend an appropriate sequence for development.

Some AI-assisted software development tools, like Claude, typically split down components, which may make code structures more modular and easier to maintain. So, use this nice prompt example or any other one you choose to create a roadmap:

Using the component breakdown we’ve created, can you help me develop a project roadmap? Please include:

1. A suggested order for developing the components
2. Estimated time frames for each component (assuming I’m working on this part-time)
3. Potential milestones or checkpoints.
4. Any dependencies between components that might affect the development order.
5. Suggestions for any proof-of-concept or prototype stages that might be beneficial 

Or, you could use it in any way. For instance:

• Can you identify potential risks based on our plan? Suggest mitigation strategies.

• We’re debating [Technology A] vs [Technology B]. Can you compare them for performance, scalability, and ease of use?

• Based on our scope, what’s the rough development time? Any red flags or areas where we might need help?

• Let’s think step-by-step about the architecture for [component]. Consider: кey functionalities, data structures, classes/modules, how it connects with others, useful design patterns, scalability concerns. Explain your thinking at each step.

This is the part when AI in software development is really useful. In real life, it has an even more modular and scalable architecture than those who have been working with it for a long time. As a result you get a detailed outline. Then you can follow up to refine, tweak, or pivot based on your goals.

5. Document Decisions

Architecture Decision Records not only record your thoughts, but they also make it simpler to hire new people and grow your business later on. AI-streamlined code is the same as regular coding, use these prompts:

• Based on what we’ve spoken about so far about the [component/system], please help me create an ADR. Include the background, the possibilities you thought about, the ultimate decision, the pros and drawbacks, any other decisions that are linked, or the compromises.

• Which design patterns might work well for us as we require [particular functionality]? Please tell us how each suggested pattern may be used in our system and what benefits it would provide.

• We need to create a database schema for [a certain element of the system]. Could you provide an initial schema based on what we need? Please list the tables, key fields, and connections, as well as any indexing methods that might assist improve performance.

• Please help me build RESTful API endpoints for [feature]. Include methods, routes, the structure of requests and responses, and authentication.

• Look over our existing architecture. Think about vertical and horizontal scaling when you suggest changes to accommodate [anticipated demand].

As you can see, the steps for starting, planning, and designing a project using AI aren’t really different from those for conventional software development. Let’s check whether this is the same for other parts of making artificial intelligence software.

Smarter Coding

It’s time to start construction now that everything is ready. But first, let’s talk about some rules for successful AI-assisted software development:

• Iterate: Let AI propose things, which will create an endless cycle of feedback.
• Check: AI makes a lot of mistakes. So copying without thinking would not help.
• Specify: The more specific your request is, the better the outcome will be.

Think of AI as a promising junior developer instead of a code genie. Like any team member, AI in software development requires clear, specific instructions to do its job.

Structure Prompts

Surprisingly, English might become the most significant programming language of the 21st century, as the quality of the code from your AI-assisted software development relies on how well you communicate. When you provide unclear suggestions, you get ambiguous outcomes. That’s why we’ve provided the format that has been shown to work:

I need to implement [feature] in [language]. Key requirements:

• [Requirement 1]

• [Requirement 2]

• [Requirement 3]

• Please think about how to handle errors, edge cases, speed up performance, and best practices for [language/framework]. Add descriptive comments in the code that describe how it works.

This framework makes sure that your helper knows not just what you want but also how to think about the issue. It gives AI software development helpers more freedom to come up with new strategies to get the best outcomes.

Review AI-Generated Code

The first guideline for working with AI-generated code is not to just copy and paste it. It’s a terrific place to start, but it’s not the end of the road. Unless you want to spend hours fixing bugs.

Also, always read the whole excerpt from top to bottom, not only the code but also the thinking behind it. You need to make sure that the code meets the criteria of your project. Before adding any code to your codebase, make sure you understand every line. If you have any queries, you may ask something like this:

• Can you explain this part in detail? Specifically, what does it do? Step-by-step, how does it work? Any risks or limitations?

• Please take a look at the following code: [paste your code here]. When reviewing, please consider:

• Code quality and alignment with best practices

• Potential bugs or edge cases

• Opportunities for performance improvements

• Readability and long-term maintainability

• Any relevant security risks

1. Implement [algorithm] in [language]. Include the main function, helper functions, complexity analysis, and a usage example.
2. Create a [class/module] for [purpose] in [language]. Include constructor, public methods, docstrings, and OOP principles.
3. Here’s a slow code snippet: [your code]. Suggest improvements and explain trade-offs.
4. Write unit tests for: [function]. Cover normal inputs, edge cases, and invalid inputs. Use [testing framework].

You may apply the same method for modernizing old systems using AI.

Proven AI-Powered Coding Process

Based on our tests, this is the best way for AI-assisted software development to help with software development:

1. Start with clear definitions: Make it clear what you want to build.
2. Make a First Draft Code: For instance, use a prompt like the one above.
3. Read it all the way through and make sure you understand every sentence. If not, ask.
4. Refine Together in Iterations: Tell AI to make things better or easier.
5. Test and Debug: Make test cases and fix any problems that come up.

Designing Databases

Schema Design

I’m creating a database for [app description].

Main entities:

[Entity 1]

[Entity 2]

Requirements: [for example, quick access to user posts]

[for example, keep track of connections between followers] [for example, handle big time-series data]

Please suggest: – Tables and columns (with data types) – Keys and associations – Suggestions for indexing – Things to think about for scalability – The reason for the design

Structure Refinement

This is my plan:

[schema]

Look at it for:

Problems with normalization

Possible advantages of denormalization

Strategies for indexing

Bottlenecks in scalability

Integrity limits 

Query Optimization

I want to make the following SQL query work better:

[Paste your question here]

The query doesn’t work well on big datasets. Please help by:

Looking at the query to see if there are any performance issues. Suggesting improvements, which might include:

Rewrites of queries

Changes to the index

Changes to the schema, if needed

Telling why each suggestion was made

Giving a general idea of how much performance may improve, if possible

More information:

Database system, like MySQL or PostgreSQL

Table sizes: For example, the Users table has around 1 million rows.

Hardware limitations, including not having enough memory or having to share resources 

Index Optimization

Given this table and query pattern: [schema + sample queries]. What’s the best indexing strategy?

Data Migration Planning

I’m migrating from this schema:  [old schema] to this one: [new schema]. Create a migration plan, including validation and integrity checks.

Query Performance Troubleshooting

This query is slow:  [query]. Here’s the execution plan:  [plan]. Suggest fixes and explain the reasoning.

Performance Tuning

This is how I put up my DB:

– PostgreSQL 13 – Size: 500GB

– Tables: [list] – Common questions: [overview]

– Pain points: [for example, joins and writes]

Suggest ways to increase performance, such as:

1. Settings that may need to be changed
2. Review of the indexing approach
3. Ways to make queries run faster
4. Possible schema improvements
5. Caching strategies
6. Any additional helpful tips for increasing performance

Please describe the anticipated effect and any possible trade-offs for each option. 

A thorough review like this provides a clear roadmap for optimizing your database and often uncovers improvement opportunities you may have overlooked.

Creating Documentation

Let’s be honest: no one wants to write things down. So, AI in software development can be very handy as it steps in to take on this part. Here are some helpful ideas for this.

• Make documentation for [project/component]. Include a summary, installation instructions, configuration choices, an API reference, use samples, troubleshooting tips, a FAQ, and an audience (e.g., developers or users). Use simple words, provide examples, and stress excellent practices.

• Review and refine the following documentation section: [Paste section here]. When reviewing, consider the following:

• Clarity and ease of understanding.

• Completeness and accuracy of the content.

• Whether the level of detail fits the target audience.

• Alignment with best practices in technical writing.

• Generate API docs for: [endpoint]
  Include method, parameters, responses, auth, examples, and rate limits.

1. Create a README for: [repo]
   Include the title, description, installation, usage, contribution, and license. Use Markdown.
2. Create a user guide for: [feature]
   Target audience: [e.g., non-technical users]
   Include walkthroughs, examples, and troubleshooting.
3. Add docstrings to this function:
   [code]
   Include purpose, params, return value, exceptions, and usage.
4. I updated this feature:
   [summary]
   Please update the docs to reflect the changes. Highlight any new features or breaking changes.
5. Review this documentation:
   [paste docs]
   Suggest updates based on best practices and common user pain points.

Check that all code snippets operate as they should. Include any details or background information about the project that the AI may have overlooked. Lastly, consider adding graphics like diagrams, flowcharts, or screenshots to your text to make it easier for other people to comprehend difficult concepts. This mentality is different from the way we write documentation for AI-assisted software development.

Maintaining

We made sensible plans. We constructed quickly. Now it’s time to improve, protect, and keep everything running. Let’s talk about how AI can help you test your code, make it run faster, keep your system safe, and even assist you in cleaning up your version control practices. It’s preferable to do one step at a time.

Testing: I need unit tests for this function: [Paste code].

Tests for happy paths, edge cases, error handling, and boundary values should all be included.

For each test, provide a brief description, describe what testing framework you used (like Jest or Pytest), and explain any mocks or fixtures you used.

For integration tests:

I need to write integration tests for these parts:

[Write out the parts and how they work together]

Please provide a group of integration tests that:

Cover important ways that the parts interact with each other

Check that errors are handled correctly and that edge situations are taken into account.

Add setup and takedown processes as necessary.

Show the test scenarios in a straightforward, step-by-step way, and include any test data or dummy objects that are needed. 

For performance testing:

I need to make a strategy for testing the performance of my app, concentrating on the following areas:

[List the main features or parts that need to be tested]

Please help me build a strategy for a performance test that includes:

Important performance parameters to keep an eye on.

Test situations that mimic varied levels of use and load.

Tools or frameworks that are suggested for doing the tests.

Ways to find and look at performance bottlenecks.

Best ways to understand the findings and make adjustments based on them. 

For test data generation:

I need to make test data for the database structure below: [Put your schema here]. Please help me establish a full strategy for generating test data that includes:

• Suggested value ranges or data formats for each field

• SQL or scripts to create a dataset with a lot of different types of data, such as:

• Common (typical) usage scenarios

• Edge cases

• Test error handling with wrong entries

• Correctly addressing referential integrity between tables that are linked

• Important data patterns or situations for testing

The dataset should be comprehensive enough to accommodate diverse test scenarios while staying lightweight and manageable. 

This makes sure that a lot of instances are covered and that you don’t overlook any. But even if AI has many advantages for testing and debugging, human judgment is still the most important thing in the creation of AI software.

Code Optimization

Now is the moment to make our code operate as well as it can. Data breaches happen all the time, and consumers have high expectations. Security and optimization aren’t just good to have; they’re required. The higher, the better.

For AI-powered security audits:

Please check the following code for security: [Paste code].

Find dangers such as injection issues, failed authentication, data disclosure, unsecured deserialization, and libraries that are known to be weak.

For each problem, talk about the danger, offer a solution, and include code samples. Suggest useful tools or libraries for long-term safety.

For performance optimization:

Look at this code to see if there are any performance problems: [Paste code]. Look for things like bottlenecks, actions that use a lot of memory, chances to do things asynchronously or in parallel, and ideas for caching.

For each concept, explain what it would do, offer better code, and outline the pros and cons.

For security updates:

As of [date], what are the most recent best practices for [language/framework]?

Pay attention to: security upgrades, performance patterns, new features, and old ways of doing things. For each one, tell us why it’s important and how to do it.

Finding potential SQL injection vulnerabilities:

Please go at the following database interaction code to see if there are any SQL injection holes:

[Paste your code for interacting with the database here] If you find any weaknesses, please:

Describe how the problem may be used in real life.

Give a safe alternative implementation.

Please suggest any tools, frameworks, or strategies that might work well with our database system to assist avoid such problems in the future.

In short, AI in software development keeps an eye on security holes, but only you know the exact dangers your program faces. You also have to find a balance between security, performance, and ease of use. What is safest or most efficient isn’t necessarily ideal for your users.

Quality Control

Don’t stop after the first try. Look over and make better:

Please go over the code below: [Paste code]. Check the code for quality and style, overlooked edge cases, performance or security problems, readability, and chances to rework. For each problem, describe what it is and how to remedy it.

Debugging

AI-assisted software development may give you a new point of view. When you’re stuck, these kinds of suggestions may help:

This is the bug I’m seeing: [Explain the problem and any error messages]

This is the code you need: [Paste code]

Please help me by listing possible reasons, suggesting step-by-step ways to troubleshoot, recommending any tools to utilize, suggesting a possible repair, and explaining it.

Version Control

AI also makes it easier for teams to work together, from clearer commit messages to easier merges. To help you make great Git commits, tell your assistant:

I made these changes: [Paste git diff or explain what you changed].

Make sure your commit message has a short topic (less than 50 characters), a clear body (no more than 72 characters), and mentions any associated concerns. It should also follow the usual commit format.

The end result? A clear, easy-to-read history that explains the tale of your project.

For creating a .gitignore file: Help me make a.[language/framework] gitignore. Don’t include system files, dependencies, or secrets, and explain any strange entries.

For writing release notes: Using this commit log: [Paste], make comments for version [X.Y.Z] that include features, bug fixes, breaking changes, and thanks.

For improving branch naming conventions: Please suggest a clear way to name branches that contains the nature of work and the issue numbers. Give examples of features, hotfixes, and refactors.

But there is one thing that AI in software development can’t accomplish. Only individuals can understand how a team operates and the context of a project, and they can also resolve merge disputes with subtlety. While AI can help with reviews, in-person discussions are necessary for decisions like design and code ownership. Don’t use AI to replace your process; use it to improve it.

Wrapping Up

This is the last part of this guide. You now know how AI speeds up your work and helps you create better software more quickly. Also, well-structured, explicit instructions let AI reach its full potential in planning, coding, testing, and working together.

AI works best when you keep trying. Don’t expect everything to be flawless. Make your prompts better. Make your outcomes better. AI didn’t replace humans; it altered the way we operate by providing me time to concentrate on what matters most: making amazing things. It helped us do things better, quicker, and with greater happiness.