Software Development in a Post-AI World

If your company doesn’t have guidelines on how you should work with AI-powered software development, you’re falling behind. This piece covers how to work with AI in three different stages of software development:

1. Idea-to-Prompts
2. Prompts-to-Prototype
3. Prototype-to-Product

• Convert ideas to PRDs using an Agent – Use AI to convert meeting notes, transcripts, and rough ideas into Product Requirement Documents (PRDs) with clear goals and technical details.
• Convert PRDs to Lovable Prompts – Train AI agents with examples from past successful Lovable-prompts to generate high-quality prompts for your prototyping process.
• Use Lovable for Prototypes – Tools like Lovable let teams turn PRDs into functional prototypes quickly, reducing bottlenecks and speeding up client feedback.
• Let non-developers create prototypes – Business developers, designers, and project managers can use Lovable and AI to build testable features before engineers even get involved.‍
• Create 10x Devs with Cursor – Cursor predicts code edits, automates debugging, and helps developers write better code faster with smart suggestions.

Capturing the essence of an idea is often the hardest part of building software. Clients come to us with rough concepts, sometimes no more than a few scattered notes, and expect us to translate them into something real. Traditionally, this has meant long meetings, endless back-and-forth, and a lot of guesswork. But with AI, we use an agent that changes how we approach the earliest stages of product development.

From Transcripts to PRDs

Our AI agent takes meeting notes, transcripts from Fireflies, and documented descriptions of products and features and turns them into Product Requirement Documents (PRDs) using a predefined template as a one-shot prompting example. 

This ensures every PRD contains the necessary elements: goals, user stories, technical considerations, and more. If key information is missing, the agent does not just guess. It generates follow-up questions that help us quickly identify gaps before they become roadblocks. What used to take hours of synthesis and revision is now automated, allowing us to turn vague discussions into structured plans.

From PRDs to Prompts

Once the PRD is generated, the next step is turning it into sequential prompts we use in Lovable (more on Lovable in the next section). The agent does not just copy and paste text. It has been trained on few-shot examples from past experiences and successful prompts, refining the input to produce better outputs. This means the prompts it generates are optimized for Lovable’s capabilities, ensuring the AI understands what to build and how. Instead of engineers spending time refining prompts through trial and error, we get structured, high-quality input from the start.

The result is a workflow that moves seamlessly from raw notes to a structured plan and directly into prototyping.

A reality of working in consulting is the reliance on clients being able to accurately describe what they want. This is much harder than it seems, so it’s no fault of theirs that this communication can, at times, be difficult.

That’s why, at Heyra, we take an MVP-oriented startup mindset when starting new projects. The ability to quickly translate ideas and notes on a napkin into working prototypes and proof of concepts allows us to put something in front of our clients early and gather their feedback. This process has traditionally been slow and expensive, requiring developers to spend weeks coding initial versions of a product.

Prototyping at 10x Speed

Tools like Lovable, V0, and Replit Agent, branded as full-stack engineer agents, enable us to increase the speed at which we move from client requests to prototypes and client feedback by a factor of ten. What once took a dedicated engineering team days or weeks to build can now be assembled in hours.

At Heyra, we use Lovable to take a loosely defined plan and turn it into working prototypes at record speed. These tools aren’t replacing our engineers but rather allowing them to move faster by automating repetitive coding tasks and filling in the gaps between idea and implementation.

Using only natural language prompts, these platforms can generate frontend designs, write functional backend logic, and integrate APIs. What once took a dedicated engineering team days or weeks to build can now be assembled in hours.

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Who’s Building Prototypes Now?

Perhaps the biggest shift we’ve seen is that AI is no longer just speeding up developers, it’s making development accessible to more people. Business developers, designers, and even project managers can now take on prototyping themselves before a single software engineer gets involved. A project manager or designer can describe a feature, and within minutes, they have a basic version of it running which is ready to be tested, iterated upon, and refined.

This reduces bottlenecks and ensures that when engineers do step in, they’re working with something tangible rather than a vague specification document. This shift is already reshaping how we develop software. Instead of waiting weeks to see a rough version of an idea, clients and internal teams can interact with prototypes within days. Feedback loops are shorter, iterations happen faster, and our team can focus on refining rather than building from scratch.

Not without Flaws

As exciting as these AI-powered prototyping tools are, they’re not without their challenges. They’re great at speeding up the early stages of development, turning rough ideas into something tangible in record time. But they aren’t magic solutions, and they come with limitations that make them less effective for more complex projects.

One of the biggest strengths of these tools is their ability to quickly generate user interfaces and basic functionality. They allow teams to test ideas much faster than before, reducing the time spent waiting for an initial version of a product. This means feedback loops are shorter, and adjustments can be made early in the process instead of weeks down the line.

However, these tools struggle when projects move beyond simple prototypes. They can be unreliable, often requiring multiple attempts to get things right. They lack flexibility when dealing with more advanced features, making it difficult to build anything that requires complex user interactions, secure authentication, or large-scale data handling.

This doesn’t mean they aren’t valuable. They’re just best used for what they do well, helping teams move fast in the early stages. When it comes to building something long-lasting and scalable, developers still need to step in to refine, secure, and optimize the final product. The real advantage is knowing where to draw the line, using these tools to get started quickly but not expecting them to do all the heavy lifting. Which is how this article pivots to the set of tools we use in the next development stage: Prototype-to-Product.

Generative AI tools like ChatGPT, Claude, and now Cursor are completely upending how we approach software engineering. What used to be a slow, iterative process is now something AI can accelerate at an almost alarming speed.

Junior Devs: Less Coding, More Prompting

Take AI coding tools like Cursor and Claude. They’re changing how new developers learn and work. Juniors who once spent months memorizing syntax and debugging errors can now generate functional code just by describing what they need in plain English. This means they can contribute meaningfully almost from day one.

But there’s a catch: Instead of building a deep understanding of programming concepts, many are becoming experts at prompting AI rather than coding itself. Stack Overflow is collecting dust while AI models serve up instant answers. The risk? A generation of developers who know how to ask the right questions but struggle to solve problems independently.

Senior Devs + AI = 10x Dev

At the same time, senior developers who know how to leverage Cursor, chatbots, and other AI tools are becoming productivity powerhouses. They are handling the workload of entire teams by automating tasks that junior developers used to manage. Soon, their role will evolve even further, with less focus on hands-on coding and more emphasis on guiding AI agents to build exactly what is needed.

One of the most powerful tools enabling this shift is Cursor. It improves development by predicting code edits, automating debugging, and providing AI-driven suggestions based on the context of the codebase. By integrating Cursor into our workflow at Heyra, we are accelerating software development while keeping engineers in control.

Cursor

Cursor functions as an AI-powered co-developer that understands our codebase, predicts changes, and suggests edits before we even type them. Unlike traditional autocomplete, Cursor is aware of the broader context, offering suggestions that align with the structure and logic of our existing code. One of its most powerful features is its ability to understand and edit code using natural language. Instead of manually refactoring an entire function, engineers can simply describe the changes they want, and Cursor will generate the necessary edits.

Cursor Features:

• Tab & Smart Autocomplete – Cursor predicts your next edit and suggests multi-line code changes in real-time, saving time and improving efficiency.
• Chat with Codebase – Ask Cursor questions about your current file, debug issues, and reference specific code sections using @Codebase or Ctrl+Enter.
• Agent Mode – Automates end-to-end coding tasks, understands context from the codebase, runs terminal commands, and loops on errors to refine results.
• Ctrl K for Editing & Commands – Quickly edit, generate, or rewrite code by selecting and describing changes, and convert natural language instructions into terminal commands.

Perplexity

While Cursor helps us write and edit code, Perplexity acts as our AI-driven research assistant. It provides precise, context-aware answers to technical questions by pulling from reliable sources across the web. This eliminates the need for endless Googling and navigating outdated Stack Overflow threads. Instead, engineers get concise, up-to-date explanations, whether they are troubleshooting a bug, learning a new framework, or looking for best practices.

Perplexity also helps us optimize our AI agent workflows by refining the prompts we use in Lovable. It allows us to quickly analyze how different prompt structures impact output quality, ensuring that our AI-generated code aligns with our expectations.

AI is not just another tool in software development. It is transforming how teams work, from structuring early ideas to rapidly building prototypes and refining code. The ability to move from scattered notes to a functional prototype in record time means faster feedback loops, more iterations, and ultimately better products. At Heyra, we see AI as a force multiplier that elevates the role of engineers rather than replacing them. Tools like Cursor and Perplexity automate repetitive work, improve code quality, and provide instant access to relevant knowledge, allowing developers to focus on high-level problem-solving.

The challenge is no longer whether to adopt AI but how to use it effectively. Companies that fail to integrate AI into their workflows will struggle to keep up with the pace of modern software development. Those that embrace it will gain a competitive advantage in speed, product quality, and team agility. Software development is no longer just about writing code. It is about understanding how to collaborate with AI, refine prompts, guide models, and use automation to build smarter and faster. At Heyra, we are not just adapting to this shift. We are driving it.