Judiciary Ready To Go Paperless, Rolls Out AI and Digital Systems - Uganda Radionetwork
Judiciary Ready To Go Paperless, Rolls Out AI and Digital Systems Uganda Radionetwork
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Building a Production RAG Pipeline: Lessons from Real-World AI Apps
Building a Production RAG Pipeline: Lessons from Real-World AI Apps RAG (Retrieval-Augmented Generation) sounds simple on paper — embed your documents, store them in a vector DB, retrieve the relevant chunks, and pass them to an LLM. In practice, getting a RAG pipeline to production quality is significantly harder. Here's what I learned building RAG pipelines for real SaaS products. The Naive Implementation Most tutorials show you this flow: Chunk your documents Embed them with OpenAI Store in Pinecone Retrieve top-k chunks Pass to GPT-4 This works fine in demos. It fails in production for a few key reasons. Problem 1: Chunking Strategy Kills Retrieval Quality Naive fixed-size chunking (every 512 tokens) destroys semantic context. A paragraph about "authentication" gets split mid-sentence,
I Made Parseltongue
Yes, that one from HPMoR by @Eliezer Yudkowsky . And I mean it absolutely literally - this is a language designed to make lies inexpressible. It catches LLMs' ungrounded statements, incoherent logic and hallucinations. Comes with notebooks (Jupyter-style), server for use with agents, and inspection tooling. Github , Documentation . Works everywhere - even in the web Claude with the code execution sandbox. How Unsophisticated lies and manipulations are typically ungrounded or include logical inconsistencies. Coherent, factually grounded deception is a problem whose complexity grows exponentially - and our AI is far from solving such tasks. There will still be a theoretical possibility to do it - especially under incomplete information - and we have a guarantee that there is no full computat
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