AI for Software Development: Best Practices and Tools
Douglas da Silva | Mar 24, 2025
The tech world is drowning in AI coding assistants and development tools, each promising to revolutionize how we build software. As a CTO who’s spent 15+ years leading development teams, I’ve seen countless tools fail to deliver on their promises – creating more distractions than solutions.
My approach is simple: Does the AI development tool make teams measurably more productive without sacrificing quality? Everything else is just noise.
After extensive testing across real software development projects, I’ve identified which AI development tools actually deliver tangible results.
No marketing fluff, no hypotheticals – just practical insights from the trenches of software engineering.
Most discussions about AI coding tools suffer from three fundamental issues:
The market is now flooded with options:
So which ones actually deliver results? Let’s cut through the noise.
After extensive testing, here are the clear winners – each with specific strengths for different use cases.
Tool | Key Strengths | Best Use Cases | Limitations |
Cursor | Enterprise-grade development assistant, multi-file context awareness, workflow optimization, style adaptation | Suitable for large and small projects, refactoring, debugging, and learning new frameworks | Requires initial project setup for optimal performance, not recommended to set projects from scratch |
Replit | Integration powerhouse, rapid prototyping, seamless external service integration | Proof-of-concepts (POCs), quick app prototypes, API integrations | Not ideal for long-term, production-grade applications |
Lovable | Design-first AI, high-quality UI/UX elements, strong visual prototyping capabilities | Early-stage UI/UX prototyping, stakeholder presentations, conceptual design exploration | Weak backend integration, not suited for large-scale product development |
LLMs (Claude 3.7/o1/Grok) | Problem-solving and architectural decision support, reasoning partner for complex technical discussions | Architectural planning, algorithm selection, technical requirement analysis | Less effective for direct coding assistance, lacks full development workflow capabilities |
What sets it apart: Cursor enhances your existing workflow rather than trying to replace it. It acts as a collaborative partner under your control, not an autopilot.
Key capabilities:
Real-world results: 20-30% (at least) efficiency gains on routine tasks like:
Best use cases:
Setup note: For optimal results, Cursor benefits from the initial configuration. Creating Cursor project rules files tailored to your project significantly improves the quality and consistency of its suggestions. Codeguide.dev can come in very handy for creating the right project specifications and rules.
Key advantage: Cursor strikes the perfect balance by enhancing developer capabilities without removing their agency or understanding. It doesn’t generate entire applications; it accelerates and improves your existing development process while maintaining code quality.
Where it shines: Excels at seamlessly integrating external services into your project.
Key strengths:
Limitations: Where Replit falls short is in building complex, production-grade applications meant to scale. It’s perfect for validating ideas and creating working prototypes, but for long-term, large-scale projects, Cursor provides better control and code quality.
Ideal use: Validating ideas and creating working prototypes quickly.
Design strengths: Consistently outperforms UI/UX quality with better visual hierarchy, spacing, and design details.
Considerations:
Optimal use case: Use Lovable early in the process when exploring design directions or creating mockups for stakeholder approval – then transition to other tools for implementation when building production-ready applications.
Problem-solving capabilities: Excel at complex architectural decisions, algorithm optimization, and understanding technical documentation.
Best applications:
Complementary approach: These models work best as reasoning partners for architects and developers. They excel at helping you think through complex problems and evaluate different approaches.
Code snippet generation: While not as powerful as dedicated coding tools like Cursor, these LLMs can efficiently generate smaller code snippets and solutions to targeted problems. If you don’t have access to specialized coding tools, they provide a decent alternative for simpler coding tasks. Cursor 3.7 and Grok really shine the spotlight on code-related questions and reasoning.
Adding these tools requires a clear strategy, not blind adoption:
My testing methodology focused on real-world applications:
For production applications and scalable products:
For POCs and rapid validation:
For architectural decisions and problem-solving:
The key is matching the tool to your specific context rather than following marketing hype.
At Cheesecake Labs, we’ve implemented these tools across a few client projects:
This AI-augmented approach has become essential in our custom AI solutions practice, benefiting staff augmentation clients with established productivity-enhancing workflows.
When implemented correctly, these tools deliver tangible benefits:
The impact is real, but it requires the right tools applied in the right way.
The AI development landscape doesn’t have to be overwhelming. By focusing on practical results rather than marketing promises, you can identify the tools that deliver actual value.
The right approach isn’t about finding magical AI that replaces developers – it’s about enhancing capabilities with tools that solve real problems.
Start with Cursor for enterprise development, leverage Replit for rapid prototyping, use Lovable for design exploration, and tap into reasoning models for complex decisions.
This field evolves rapidly, but my approach remains constant: evaluate tools based on measurable productivity improvements in your specific context, not on promises or hype.
Next in this series, I’ll tackle another area where AI claims revolutionary potential: MVP development. We’ll separate genuine game-changers from empty buzzwords in early-stage product development.
Douglas started as a Senior FullStack Developer at Cheesecake Labs and currently he's Partner and CBDO at the company.
Marcelo Gracietti | Mar 07, 2025