In this episode of Learning Meets AI, Saad shows how Learning and Development professionals can move from experimenting with AI tools to building practical products that solve real work problems. He shares how he first used ChatGPT to review coaching transcripts, then walks through a live build of a jargon analysis tool in Lovable, designed to help technical professionals communicate more clearly with non-technical stakeholders.

What You’ll Learn

1. How Saad used AI to reflect on his own coaching practice and turn a manual prompt into an automated tool
2. A practical four-step workflow for building an AI-powered product, from user flow to backend
3. Why planning before prompting can save time, reduce rework, and improve build quality
4. How AI can support L&D work through discovery notes, workshop design, visuals, and thought partnership
5. What Saad learned from building live with Lovable, including testing, debugging, and isolating changes
6. Why the best AI product ideas often come from your own day-to-day pain points
7. How L&D professionals can approach AI without getting overwhelmed

Tools Referenced

• ChatGPT
• Gemini
• Claude
• Evernote AI
• Lovable
• Firelfly
• Otter
• Whisper AI
• Replit
• Supabase

Key Takeaways

1. Start with a real problem you understand deeply. Saad’s strongest product ideas came from his own work as a communication coach, not from trying to force a use case. His jargon analysis tool emerged from a real coaching challenge, helping engineers communicate more clearly with non-technical stakeholders.
2. Map the user flow before you build. Saad emphasizes thinking through the user journey first, step by step, before touching the product builder. That clarity makes it easier to write a focused PRD and helps the AI generate something closer to what you actually need.
3. Use AI as a co-creator, not a replacement. Saad uses tools like Claude, Gemini, and ChatGPT to support workshop design, summarize discovery notes, and sharpen ideas, but he does not hand over ownership of the work. His approach is to augment judgment, not outsource it.
4. Front-end is the easy part, backend is where the real work begins. Saad’s live build quickly produced a clean interface, but the harder part was making the app actually function, connecting the backend, testing analysis logic, and debugging why the jargon terms were not showing up properly.
5. Prompting improves when you think like a product designer. Saad shows that good prompting is not just about clever wording. It is about defining users, goals, inputs, outputs, and constraints clearly enough that the AI can build with direction.
6. Testing live reveals what planning missed. During the build, Saad discovered gaps such as missing transcript upload options, unnecessary diarization, and analysis bugs. Those moments reinforced that iteration is part of the process, not a sign that the build failed.
7. L&D needs to stay closer to customers and be simpler in its language. Saad argues that the field can get lost in complexity. He believes L&D creates more value when it understands customer needs, speaks the language of the business, and focuses on practical outcomes rather than training for its own sake.
8. People who feel overwhelmed by AI should start small and build confidence through use. Saad’s advice is to begin with one clear use case, experiment directly, and apply critical thinking to both AI outputs and the wider conversation around AI. For him, confidence comes from doing, not from watching from the sidelines.

Follow Saad:

• LinkedIn: https://www.linkedin.com/in/saad-m-qureshi/