6d ago · 20 min read · ⚡ TLDR: RAG in 30 Seconds TLDR: RAG (Retrieval-Augmented Generation) fixes the LLM knowledge-cutoff problem by fetching relevant documents at query time and injecting them as context. With LangChain you build the full pipeline — load → split → embed...
Join discussionMar 27 · 6 min read · Why Vector Databases Matter – My Deep‑Dive into ANN Indexes and Metrics I spent the afternoon wrestling with a simple RAG prototype. I could generate embeddings with sentence‑transformers in a few seconds, but as soon as I tried to query a few thousa...
Join discussionMar 24 · 10 min read · Code reviews are one of those things every team agrees are important but nobody enjoys waiting for. You open a pull request, your reviewer is heads-down on something else, and the PR sits there. When
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Mar 23 · 5 min read · How AI finds answers — and why the next generation is rethinking the approach. Introduction LLMs are powerful, but they only know what they were trained on — once training ends, new documents, compa
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Mar 17 · 18 min read · When I last wrote about this project, I was benchmarking enterprise AI inference tooling against a local alternative on cutting-edge GPU hardware — and discovering that enterprise frameworks are not a
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