Something I have been noticing during interviews recently. A lot of freshers and junior engineers say they want to build a career in AI. But when I dig deeper, only a few seem interested in understanding how things actually work behind the scenes. They spend time learning Python, building projects, understanding RAG, agents, model limitations, debugging issues, and figuring out why something works or doesn’t work. Many others seem to be focused on learning high-level concepts, prompt engineering, and building demos using low-code or no-code platforms. There is nothing wrong with that, and these tools are great for getting started. But I wonder if it is creating a gap in problem-solving ability. For example, I often see candidates who can explain what an agent is, what RAG is, and what tools like LangChain or CrewAI do. But when asked to design a solution, troubleshoot a failing workflow, handle edge cases, or write code, they struggle. Maybe this is just what I am seeing, so I wanted to ask the community: Are you seeing the same trend? Do you think low-code/no-code AI platforms are helping people learn faster or skipping too many fundamentals? For someone starting their AI career today, what skills will matter most in the next 3–5 years? Will strong software engineering and problem-solving skills continue to be the key differentiator? Interested to hear thoughts from hiring managers, senior engineers, and people who are currently learning AI. submitted by /u/Exciting-Sun-3990
Originally posted by u/Exciting-Sun-3990 on r/ArtificialInteligence
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