AI Development Tools We Use to Speed Up Development Process
- Created: Jan 31, 2025
- 15 min
CEO
Artificial Intelligence has surged into our daily lives. And software development is not an exception.
According to a GitHub survey, in 2023, 92% of developers were using AI tools at work. 70% of them see the benefits of utilizing AI coding tools.
Developers surveyed highlight that AI solutions can enhance their performance and improve collaboration.
At SpdLoad, we have been utilizing AI tools to enhance development workflow and speed up our SaaS development services.
Here, we’ve gathered some of the best AI tools we use for code generation, debugging, and knowledge assistance.
We also explore some of the benefits of using AI tools for software development.
Drop us a line and we'll get back to you immediately to schedule a call and discuss your needs personally.
Why Developers Use AI Tools
The answer is pretty simple: to fasten things up without compromising on quality.
So, instead of spending hours on debugging or searching for the right library, developers use AI tools that offer instant solutions, code completion, and even automate some parts of the development process.
This frees up time for more important tasks that involve rapid iteration, creative thinking, and high-level problem-solving activities.
However, we must note that AI developer tools will not replace developers.
These instruments only augment their abilities, not fully substitute them.
AI coding assistants help developers and product teams accelerate software development with Al-enabled code generation and coding assistance and ramp up on new languages and code bases. 
What are AI Development Tools?
According to the AI glossary, AI developer tools are software applications that use artificial intelligence and machine learning to enhance the development workflow.
These tools are used to automate repetitive tasks, reduce the chances of human errors, provide code suggestions, and improve productivity across teams.
A study on the effects of generative AI on high-skilled work revealed that developers complete 26% more tasks weekly when using Al-based coding assistants.
Specifically, the study highlights a 13.55% increase in the number of code updates and a 38.38% increase in the number of times code was compiled.
There are many companies that have already integrated advanced AI tools into their development routines.
Regnology, for example, built its Ticket-to-Code Writer tool with Gemini 1.5 Pro to automate the conversion of bug tickets into actionable code. This solution helps them significantly streamline development.
Another example is Turing, an AGI infrastructure company. They saw a 33% developer productivity gain while using Gemini Code Assist in their development teams.
Our dedicated software developers also utilize AI tools to streamline the development workflow.
In the next sections, we will explore the tools they use.
Benefits of Using AI Tools in Development
We have already mentioned some of the benefits of using AI tools for developers.
Let’s just elaborate more on them.
Increased Productivity
According to the Cortex State of Developer Productivity Report, 58% of respondents mentioned developers lose over 5 hours weekly on unproductive work.
Add to this number of hours of repetitive tasks like writing boilerplate code, creating APIs, or managing test cases, and you will get 2-3 unproductive work days a week.
The loss of productivity can translate into financial losses.
However, AI tools can help handle some of the mundane jobs with precision.
Hence, developers have more time to focus on solving complex problems or designing innovative features.
When automation takes care of the groundwork, project timelines shrink, and team output significantly increases.
Reduced Chances of Human Error
AI tools analyze code as you write it. They catch bugs, syntax errors, or inefficiencies instantly.
Additionally, these tools can also suggest optimizations to align the developer’s work with best practices.
This translates into fewer debugging sessions and more confidence in the stability of the code.
Over the course of time, AI tools usage helps developers hone their skills and write cleaner, more maintainable code.
Simplified Learning Curve
Developers often find it daunting to start with a new language or unfamiliar project.
AI tools for developers can simplify this learning curve.
They provide step-by-step examples, translate code snippets, or even generate helpful documentation.
This makes it easier for new team members to onboard and start contributing.
It reduces downtime and accelerates team synergy.
Enhanced Collaboration
Collaboration is a vital part of development.
And AI tools for developers can enhance it through improved workflows.
For instance, when submitting pull requests, AI can automatically review code, highlight problem areas, and propose changes.
This minimizes back-and-forth between team members and allows everyone to stay on the same page and maintain momentum.
Faster Debugging and Testing
AI-powered debugging tools don’t just find bugs.
They often explain why they happen and suggest fixes.
For testing, AI can auto-generate cases based on application requirements to ensure comprehensive coverage without manual effort.
This frees up time on more important tasks like refining features instead of chasing elusive errors.
Feel free to learn more on why is software testing important.
More Organized Project Documentation
Keeping project documentation in order is often a headache for developers.
With AI tools, developers can automatically sync documentation with code changes so that the team and stakeholders always have the latest information.
This reduces confusion, saves time, and makes the project easier to maintain in the long run.
Innovative Problem Solving
AI tools bring a fresh perspective to problem-solving.
Analyzing large datasets or exploring patterns in your code can suggest creative solutions that might not be immediately obvious to human developers.
Overall, AI tools should be considered as partners in development.
They enable development teams to work more efficiently, maintain higher standards, and create better software.
There are many use cases of generative AI in development.
In the next section, we elaborate more on the use of AI in development, testing, and documentation. 
Generative AI Use Cases in Software Development
Developers use AI not only to generate code.
In fact, code generation is one of many use cases of AI tools in software development.
Let’s explore them all.
AI in Development
| Use Case | Description | Benefits |
|---|---|---|
Code generation from scratch | AI can take a simple description of a task and turn it into an initial piece of code. | Faster project initiation. Less time spent on repetitive tasks. |
Code completion | As a developer writes code, AI can suggest how to complete the next part, using the context to predict what you need next. | Code completion function saves time. Reduces mistakes. Helps stay in the flow without interruptions. |
| Code review and error detection | AI scans code on the go, catching syntax errors, bugs, or performance issues, and gives feedback right away. | Ensuring code is clean error-free, and more efficient. |
Code documentation | AI automatically adds comments, explanations, or summaries to help clarify complex sections of the code for others. | Makes code easier to understand, share, and collaborate on. |
Code extension | AI can look at the existing code and suggest ways to expand or improve it. Can propose optimizations or new features based on patterns. | More flexible and scalable code. |
Learning new languages | If switching between major programming languages, AI helps developers translate code snippets and offers examples, making the learning curve easier. | Speeds up learning new languages and tools. |
Refactoring and optimization | AI reviews the current code to find ways to make it cleaner and more efficient. | Improved code quality. Eliminated chances of technical debt and performance issues. |
Custom code templates in multiple programming languages | AI generates code templates and snippets for recurring tasks, so developers don’t have to write them from scratch every time. | Saved time. Increased consistency across projects. Reduced repetitive work. |
AI in Testing
Use Case | Description | Benefits |
|---|---|---|
Creating testing requirements | AI uses Natural Language Processing (NLP) to dig through requirement documents and catch any vague language or logical gaps that could lead to issues. | Teams start with clear, unambiguous requirements. Less chances of misunderstandings later. |
Generating test cases | AI automatically creates test scenarios based on functional requirements and methodologies. | Frees up time for more complex testing. Ensures thorough test coverage across the board. |
Generating unit test cases | AI takes a look at your code and figures out how to test individual components, even recommending tricky edge cases to ensure full coverage. | Makes individual components more reliable and helps spot potential issues early. |
Continuous testing in DevOps | AI keeps an eye on your code as it evolves, running tests automatically within CI/CD pipelines to catch bugs as soon as they appear. | Increases the speed of releases while maintaining high-quality code with fewer surprises. |
AI in Documentation
Use Case | Description | Benefits |
|---|---|---|
Automated documentation creation | AI can generate detailed technical guides, API docs, and specifications directly from your code or user stories. | Saves hours of manual effort. Ensures your documentation is complete and consistent every time. |
Error detection and clarity | AI editors can catch grammar mistakes, improve readability, and ensure your docs follow industry standards. | Delivers polished, professional documentation that makes you look good and builds user trust. |
Real-time updates | AI keeps an eye on your codebase, automatically syncing changes to the documentation so nothing falls through the cracks. | No outdated docs, everything stays current. |
Onboarding support | AI creates tailored guides for new team members to walk them through what they need to know based on their specific roles or responsibilities. | Speeds up the onboarding process. Helps new hires feel confident and productive right away. |
AI Tools We Utilize to Speed Up Development
When you hire AI developers at SpdLoad, you can be sure they will utilize some of the best AI tools to enhance software development.
After trying various solutions, they have selected the top ones based on personal experience.
They prioritized tools that support auto-completion, assist with research, integrate with IDEs, and overall help make their lives just a little bit easier.
Here are some of the best AI developer tools they use.
GitHub Copilot
GitHub Copilot is one of the top AI tools for developers that really saves a lot of time.
It looks at what you write and suggests ready-made solutions — from simple functions to entire classes.
You don’t need to spend time writing basic code like validations or APIs. It can be done in seconds.
Copilot can generate entire lines of writing code or blocks of code, including functions and algorithms.
This saves developers time and effort as they don’t need to write repetitive or complex code and allows them to focus on genuine problem-solving and collaboration.
According to the survey conducted by GitHub, developers who use Copilot indicate up to 75% greater job satisfaction compared to those who do not use it.
Additionally, they are up to 55% more efficient in coding without compromising quality.
This translates into faster delivery.
GitHub Copilot works seamlessly with top editors such as Visual Studio Code, Visual Studio, JetBrains IDEs, and Neovim.
Copilot is, roughly speaking, ChatGPT 4, which is active within the code environment.
It often does a good job of giving you an example of how to start writing code by function name and language.
Sometimes it provides the entire code if it’s an easy function.
There is also a live chat available, so you can just ask questions. It can also help you generate documentation or improve your tests.
Criteria | Details | Impact |
|---|---|---|
Ease of Use | Works effortlessly with popular code editors like Visual Studio Code, JetBrains, and Neovim, with a quick setup process. | Makes it easy for developers to jump in and start using it without a steep learning curve. |
Automation Scope | Can write anything from a simple validation function to entire classes or algorithms. | Saves developers from spending time on repetitive coding tasks, allowing them to focus on creative problem-solving. |
Productivity Boost | Surveys show developers using Copilot are up to 55% more efficient in coding tasks. | Enables faster project delivery without sacrificing code quality. |
Real-Time Features | Provides suggestions for code completion, fixes bugs, generates documentation, and even includes live chat support. | Enhances productivity by solving issues or suggesting improvements instantly, right in the code editor. |
Advanced AI Model | Powered by GPT-4, it offers precise suggestions based on context and code intent. | Ensures developers get relevant and optimized solutions, even for challenging problems |
Claude Sonnet
Claude is an AI assistant that helps to quickly find bugs in the code that can take several hours to find.
It not only finds bugs but also explains how to fix them, helps you write tests, understand other people’s code, and create documentation.
You just send it code or questions and get a clear answer with examples.
Globally, it helps a lot in writing tests/custom scripts in situations where you have to work with a programming language you don’t know (legacy repositories, microservices), and it is also very important that a developer can learn from AI by asking for best practices.
Our developers use it to generate code or ask about architectural solutions for different tools.
Basically, it has a very clear and user-friendly interface, and our developers love to use this AI tool because it has a model that has been recognized as the most advanced among its competitors.
This tool can be instructed and provided with the relevant tools to independently write, edit, and execute code with sophisticated reasoning and troubleshooting capabilities.
Additionally, Claude Sonnet easily handles code translations. That makes this AI tool particularly effective for updating legacy applications and migrating codebases.
Criteria | Details | Impact |
|---|---|---|
Bug Detection | Quickly identifies bugs and provides clear explanations for how to fix them. | Saves developers hours of debugging and helps maintain project momentum. |
Code Understanding | Makes complex or unfamiliar code easier to digest and explains it step-by-step. | Helps developers working on legacy systems or learning new programming languages. |
Test Writing | Can create detailed, customized tests for various programming scenarios. | Improves test coverage and reliability, ensuring better software quality. |
User-Friendly Design | Designed with an intuitive interface that’s easy to navigate. | Makes it accessible even for developers new to AI tools. |
Modernization Power | Capable of translating, updating, and optimizing codebases with advanced reasoning capabilities. | Streamlines the process of updating legacy systems or migrating applications to modern platforms. |
Perplexity
Another good AI tool for developers is Perplexity.
This is a search engine that uses generative AI to provide users with instant answers to their questions in natural language.
To provide its natural language processing capabilities, Perplexity uses different large language models, including GPT-4, Claude 3, Mistral Large, and Perplexity’s own custom models.
The PRO version allows you to use different LLM models and choose the right one for some tasks, such as code refactoring and bug finding.
It uses these LLMs both to understand exactly what you’re asking it and to summarize the relevant answer.
Similarly, it has some kind of built-in search engine that it uses to find and index sources
It’s also pretty good at searching for educational information on the web, as well as subreddits on Reddit.
It uses these LLMs both to understand exactly what you’re asking it and to summarize the relevant answer.
Similarly, it has some kind of built-in search engine that it uses to find and index sources.
The main feature of this tool is that it has a chatbot-like interface that allows users to ask questions in natural language, and the AI responds with a summary containing source citations.
This is strikingly in contrast to how we are used to searching on Google, which just bombards us with a list of relevant pages but nothing more than that.
If you need to integrate NLP into your software solution, feel free to explore our natural language processing services for startups and enterprises.
Perplexity
Benefit | Details | Impact |
|---|---|---|
Simplified search | Combines AI with a search engine to give clear, concise answers, complete with source citations. | Provides focused, relevant information. |
Model flexibility | Uses multiple AI models for tasks like bug fixing and code refactoring. | Adapts to a wide range of development needs. Versatile for various coding tasks. |
Natural language queries | Allows you to ask questions conversationally and delivers straightforward explanations with examples | Simplifies research and troubleshooting. Makes it accessible to developers of all levels. |
Educational resource | Can find and summarize insights from forums like Reddit. Great for learning and exploring best practices. | Provides in-depth learning opportunities and solutions that go beyond standard search engines. |
Efficient workflow | Quickly answers coding questions, summarizes technical documentation, and helps with debugging. | Speeds up the development process. Reduces the time spent on research and troubleshooting. |
Future of AI Tools For Developers
Looking ahead, AI is expected to become deeply embedded in software development processes.
According to AI statistics, approximately four in five businesses consider AI to be a major priority in their business strategy.
Companies like Microsoft are investing heavily in AI. They establish new engineering groups to integrate AI capabilities across their tools and platforms.
Their vision includes AI agents automating complex tasks and building custom applications.
This is going to change the way software is developed.
The use of AI in software development is primarily focused on testing, quality assurance, and security vulnerability detection.
However, generative AI (GenAI) is expected to significantly enhance these processes and introduce unprecedented capabilities.
75% of software executives have seen a 50% reduction in development time by implementing AI and automation, as reported in a survey by OutSystems and KPMG.
Early adopters plan to increase AI use in other stages of the Software Development Life Cycle (SDLC), such as user interface design, code generation, DevOps optimization, and application maintenance.
71% of respondents plan to incorporate AI into application development and SDLC management workflows. Confidence in the quality of AI-generated code has risen, but risk awareness remains.
With AI strategy in SDLC processes, 56% of respondents expect higher application quality, fewer bugs, and improved performance.
Overall, these tools automate routine tasks, allowing developers to focus on more complex and creative aspects of their work.
Get a strategic MVP to mitigate risks.
The State of AI Adoption Across Organizations
Over the course of 2025, organizations will shift from experimentation to scaling Al across operations, focusing on measurable outcomes.
According to Andreessen Horowitz, the $250 billion business process outsourcing (BPO) market is ripe for specialized Al automation.
They identify two key areas of opportunity: horizontal Al enablers that execute a specific function for a broad range of industries, and vertical automation solutions that build end-to-end workflows tailored to specific industries, such as retail operations or healthcare assistance.
During this phase, organizations have started to establish risk management, security, cost control, and overall governance that are foundational to successful Al adoption at scale.
Best Practices for Using AI Tools in Development
As I already mentioned, we actively utilize AI tools in daily coding tasks to enhance productivity and speed up development, project management, and the entire collaboration process.
Here are some of the best practices from our team on AI integration into development workflow:
Scoping and Analysis
AI processes complex details and identifies priorities quickly. This gives us a clear plan for the project without wasting time.
When we started new projects, our team often faced the challenge of sifting through complex requirements and prioritizing tasks.
After incorporating AI tools, we’ve turned this into a streamlined process.
AI helps us analyze project details, identify key features, and even highlight potential risks, giving us a clear roadmap to follow.
This means less time spent in meetings and more time spent making progress.
Gathering Requirements
With AI, we gather relevant data in a short amount of time.
It helps avoid delays and speeds up the process.
The tools we use to extract and organize relevant data from multiple sources and help us spot gaps or inconsistencies early on.
This has saved us weeks of work on large projects and allowed us to focus on refining the vision rather than hunting for details.
Creating Test Cases
AI tools are great assistants in creating tailored test cases.
This ensures thorough testing and speeds up testing cycles.
Coding
Sometimes, we let AI handle repetitive tasks, like creating validations or boilerplate code.
For example, when working on a healthcare app development, developers can use AI to generate. This will allow us to focus on building unique features and solving complex problems. This division of labor sped up our timelines without sacrificing quality.
Writing Documentation
Whether it’s API documentation, user manuals, or technical guides, the tools we use create consistent, detailed drafts in minutes.
AI can even keep the documentation updated as we iterate on the code so that there is no time wasted on redundant edits.
Verification & Validation
AI flags issues as code is written. The team can address them right away, avoiding delays later in the process.
Legacy System Modernization
AI analyzes existing code, highlights needed changes, and speeds up system transitions for better efficiency.
Integrating AI powered tools into our software development workflow, we’ve been able to tackle projects faster, with fewer errors and a lot more efficiency. These tools have not only optimized our processes but also allowed us to deliver higher-quality products to our clients.
To learn more about AI integration into your business routine, check out this article where our CEO tells about how AI can help your startup. There, he covers the 10 best ways of utilizing these tools for data analytics, sales, email marketing, and more. 
Explore our SaaS services today
Conclusion
The AI revolution has just begun, and we are lucky to be at the frontier of this evolution.
Tools with AI-powered features stand as the go-to for developers seeking to remain at the forefront of innovation.
Embracing these AI tools is not just about keeping pace with technological progress; it’s about redefining the boundaries of what is possible in software development.
As we move into the future, AI-powered software is already transforming how businesses operate.
From automating customer support to improving sales, marketing, cybersecurity, and development, AI tools are becoming essential in driving efficiency and growth. The key takeaway? AI is no longer a luxury — it’s a necessity.
However, integrating AI into your business can be a daunting task.
You need the right tools, the right approach, and a reliable software partner that can help make it all come together seamlessly. That’s where SpdLoad comes in.
As a company that actively uses AI tools for various activities, we understand the challenges businesses face when adopting AI. And we can help you make the process simple and straightforward.
Contact us and let’s discuss how our best practices of using AI tools can help you deliver your products faster, better, and more efficiently without compromising on quality.
