{
  "id": 10143084,
  "title": "What I’m Learning While Building AI-Powered Applications",
  "url": "https://urgent.news/2026/09/27/what-im-learning-while-building-ai-powered-applications",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-27T04:56:22.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/codemaestro106/what-im-learning-while-building-ai-powered-applications-3kif"
  },
  "original_language": "en",
  "account": "My recent experience building AI-powered applications has revealed that while adding an LLM API is relatively straightforward, creating a trustworthy AI feature that users can rely on within a real product is far more challenging. A recent project we undertook involved developing a Smart Upload workflow for energy and compliance data inside a SaaS platform. The initial concept seemed simple - upload CSV/Excel files, let the AI read them, and create structured data from the results. However, the true complexity of the workflow became evident upon closer examination.\n\nThe workflow evolved to include multiple stages: upload the file, let the AI analyze the data, review the AI's findings, make corrections if needed, re-analyze the file with the corrections in mind, validate the final output, and then import the data into the system. One of the key lessons we learned is that AI output should not be directly accepted as application data. Instead, we treat the AI's results as a proposal that requires user review.\n\nHuman corrections are invaluable as they serve as context for the AI. If the AI misinterprets something, the user can provide instructions such as clarifying the unit of measurement or specifying the reporting period. The system can then re-analyze the file while preserving the user's previous corrections. This approach transforms the user experience from a frustrating cycle of \"AI got it wrong, start again\" to a more collaborative process where \"AI and the user are progressively improving the result together.\"\n\nFurthermore, the context in which the AI operates is crucial. We're experimenting with chatbot-style functionality within the platform. The most useful version of a chatbot isn't one that knows everything; rather, it's one that understands the user's position in the workflow, the organization they are working with, the data that already exists, the user's role, and which application tools they are authorized to use. AI must be integrated with normal software engineering practices, including validation, permissions, audit history, structured schemas, error handling, and deterministic business rules. The LLM is just one component of the system, not the entire system itself.\n\nThe biggest takeaway from this project is that good AI products are not about generating answers but rather designing a reliable collaboration between AI, application data, and the user. As I continue to learn in this field, I remain particularly interested in structured outputs, tool use, agents, and how to effectively integrate AI into real business workflows.",
  "summary": "One thing I’ve learned recently: Adding an LLM API is the easy part But building an AI feature that people can actually trust inside a real product is much harder. In one of the SaaS platforms I’m working on, we built a Smart Upload workflow for energy and compliance data. The basic idea sounds simple: Upload CSV/Excel → AI reads it → structured data is created. But the real workflow became much…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}