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Hi Contra people,
As you know RAG/LLM systems look easy in demo mode. The real learning starts when they hit production. 😅
I recently wrote a Medium article sharing some of my lessons learned while building and deploying RAG/LLM systems — the unexpected challenges, trade-offs, and things that only become obvious once real users and real data enter the picture.
Now I’m curious to hear from the community:
What was your latest “Aha!” moment with RAG/LLM systems?
• What surprised you the most?
• Which approaches worked (or didn’t)?
• What would you do differently next time?
Would love to exchange practical lessons from real-world deployments 👇
#LLM #RAG #GenAI #AIEngineering #MachineLearning #MLOps
medium.com
Building an Intelligent FAQ Assistant: The Complete Production RAG Pipeline
Moving an enterprise Retrieval-Augmented Generation (RAG) system from a functional codebase to a resilient production service is…
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I was working on a piece with AI when I caught myself asking:
“How do we make it more human?”
I stopped.
Said the human to the robot.
It sounded absurd enough that I had to make something out of it.
Hey! I just shared a write-up on something I learned while rebuilding a product with AI agents:)
The main idea is pretty simple:
Don’t put all your product context in one giant agent file.
Keep the main file small, and put the real product knowledge inside structured docs that the AI can navigate when it needs them.
This has helped me a lot when working on a SaaS product with a lot of business logic!
Sharing the full breakdown here:
I'm offering consulting services to set this up! :)
The table-of-contents-in-the-main-file pattern is the key move here - it keeps the agent's default context small while still giving it a map to go fetch the architecture or naming-convention doc only when the task actually touches that domain.
