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The LLM-Driven Software Revolution: A Paradigm Shift and the Reimagining of Product Goals
The LLM-Driven Software Revolution: A Paradigm Shift and the Reimagining of Product Goals
Fundamental Shift in Development
Software development has completely and fundamentally shifted from writing code to system integration.
Specifically, a programmer's job is to select appropriate tools and systems for technical implementation, combining them into the final product. The actual code writing is only a small part, and it's generated by AI.
This is because AI has greatly increased everyone's productivity - anyone with ideas or algorithms can create a product using AI.
New tools are emerging at a rate 10 times faster than before, making it unnecessary to develop tools from scratch.
However, integrating tools remains challenging.
Integration Process
The specific steps are: understanding tool principles, selecting and evaluating tools, designing architecture for tool integration, customizing tools, implementing integration, testing effectiveness, and monitoring the tool or related subsystems' operation.
Overall, it's about building an LLM layer beneath traditional webapp, composed of agents.
The LLM layer processes user input in specific vertical domains, integrating and visualizing information based on large models and domain-specific knowledge analysis.
Current Development Trends
Currently, popular development directions include: First, empowering existing apps with AI, such as PowerPoint generating pages through AI. Second, developing AI Agents-based apps or SaaS solutions that are completely AI-centric.
The biggest threat to AI app development comes from large model companies like OpenAI, Gemini, and Claude. Their model updates could render certain apps meaningless.
The opportunities in AI app development lie in vertical specialization and information asymmetry against large model companies.
Ordinary people's use of AI falls far short of AI's true capabilities. Therefore, marketing AI apps to specific groups can attract users.
AI Integration with Traditional Apps
My views on AI + traditional apps:
AI integration with current office tools like Office suite, WPS, email is poor, even extremely backward.
Only code editor products are well-integrated with AI, showing both programmers' advantage and how challenging it is to AI-enable a traditional app.
Taking the code editor Cursor as an example, the best UI pattern it discovered for AI + traditional apps is placing AI chat area parallel to traditional work area, rather than adding AI function buttons, prompts, or input boxes within traditional tool areas.
The Role of Data in AI Applications
My views on AI + data:
Collecting extensive specialized data and getting real user input is essential for AI to make good decisions. For example:
A founder surveyed 350 sales teams to understand sales representatives' real pain points and spent a year developing a software product.
Pocus. com is a group of AI agents that monitor and understand their own business and potential customers' business, telling sales representatives what to do next.
For instance, who to contact first, when to contact, and how to contact them. This product achieved $1 million ARR (Annual Recurring Revenue) within a year.
Success Patterns and Key Considerations
The common ground between these products is that AI solved problems users couldn't easily solve before, and AI provided practical advice.
They have very specific requirements for user input - one for how to write code, another for how to track customers. The question domain users ask when using these apps is small and specific, but the AI's processing work is complex.
The former involves vast code and complex architecture. The latter involves user company background, user customer background, and user marketing progress.
This requires writing highly effective prompts, designing sophisticated agents, and possibly enabling LLM with proprietary RAG or fine-tuning.
However, the key is naturally limiting users' question domain - broad questions can't lead to the problems the app excels at solving. Moreover, AI apps must solve a specific type of problem.
Technical Stack Overview
At the end of this article, I list some common tools for developing AI applications:
Frontend: NextJS, Tailwind, shadcn
Backend: AWS, Vercel, FastAPI, Node.js
Database: PostgreSQL, Chroma
Agent frameworks: LangChain, AutoGen, OpenRoute
Models: OpenAI, Gemini, Claude, DeepSeek, Ollama
RAG tools: LlamaIndex, GraphRAG, RAGFit
Model monitoring: Langfuse, LangSmith
© 2026 Yong Wang