The three critical competencies reshaping AI hiring in 2025: LangChain for agent orchestration, RAG for knowledge grounding, and MLOps for production deployment. Complete guide with learning roadmap.
“By 2026, more than 80 % of enterprises will have used generative‑AI application programming interfaces (APIs) or deployed generative‑AI‑enabled applications in production, up from less than 5 % in 2023.” — Gartner press release gartner.com. The pace at which generative AI (GenAI) is being adopted dwarfs previous enterprise technology waves. With hyperscalers offering managed large‑language models on demand, regulatory frameworks taking shape and off‑the‑shelf design patterns such as retrieval‑augmented generation (RAG) becoming mainstream, generative AI is moving from pilot projects to production infrastructure. This article synthesizes research findings and outlines what enterprises should expect as adoption heads toward 80 % over the next year. ...
Everyone talks about RAG, but few have actually built one. Let’s break the spell and implement a semantic search system step by step using Python and Qdrant.
5 Things I Learned Building Qdrant + RAG That Aren’t in the Documentation You want to design a Qdrant + RAG system. This means taking your documents, breaking them into pieces, converting them to vectors, storing them in a vector database, and pulling them out when needed using “cosine similarity.” So you’re not teaching the system anything - you’re just building your own smarter DB system. Wait a minute, wouldn’t it be pretty much the same if you just trained an LLM? Yes, exactly… Our learning mechanism isn’t that different either. How many of us question what we’ve learned and go after something better? Or how many of us reject information we’ve already learned? Answer: none of us. ...