Why Everyone Gets the “Essential AI Engineer Stack” Wrong

Author(s): Mayank Bohra

Originally published on Towards AI.

It’s not about the number of tools you know, it’s about the ones you use to build production systems.Image by Author

Let’s be honest. The AI space moves faster than most of us can keep up with. One week it’s all about fine-tuning, the next it’s agentic workflows and RAG. If you’re trying to build anything real, anything that goes beyond a cool demo or a local script, focusing on all the shiny new things is a recipe for burnout.

I see engineers get lost trying to learn 20 different frameworks or libraries, chasing every trend. What separates the folks who build production-ready AI systems from the rest isn’t how many libraries they’ve heard of, but how well they can wield a core set of reliable tools to solve actual problems.

If I had to boil it down to the essentials for an AI application engineer building today — and looking ahead to mid-2025 — it’s not a list of any libraries. It’s a focused stack of maybe 10, chosen for their utility in getting GenAI applications from prototype to production. These are the libraries I consistently rely on when tackling real-world challenges.

Here are the Python tools that I believe form the bedrock for building robust, scalable GenAI applications right now:

This isn’t an exhaustive list of all AI libraries. This is… Read the full blog for free on Medium.

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Published via Towards AI