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Abstract Traditional RAG pipelines usually work deterministically by using only static vector search[cite: 1]. Because of this, vocabulary mismatche…
By Tim O. I am studying for the AWS Certified AI Practitioner exam, and I wanted something to build rather than another set of flashcards. The 2026…
In my first post, I wrote about why I spent my first week writing zero business logic and instead built rig - our lightweight, POSIX-compliant local…
Under every agent memory launch, the same comment appears: "so it's RAG with extra steps." Instead of arguing, we opened the shipping source of mem0…
Why the pipeline, not the model, is usually to blame If your LLM feature starts hallucinating or your inference bill doubles overnight, the model is…
Most LlamaIndex setups end up with two separate backends once you go beyond plain vector search: a vector store for VectorStoreIndex , and a separat…
Source: https://pageindex.ai/blog/ocr Vector RAG: Why It’s Winning in Production In a world where LLMs are the new CPUs, the bottleneck isn’t the mo…
Привет, Хабр. Меня зовут Сергей Врулин, я Team Lead в команде агентов VK AI Space — корпоративной платформы для создания и запуска AI-агентов. В июл…
Привет, Хабр. Меня зовут Сергей Врулин, я Team Lead в команде агентов VK AI Space — корпоративной платформы для создания и запуска AI-агентов. В июл…
A user opens a project with nine files in it, types the most obvious question anyone types at a document app — "what are these documents about?" — a…
We’ve all been there: staring at a cluttered medicine cabinet, holding two different blister packs, and wondering, "Can I take these together?" In t…
A RAG prototype takes an afternoon. Chunk some documents, embed them, stuff the top matches into a prompt, ship a chat box. It works. You demo it, e…
TL;DR: A Knowledge Base in ByteChef is a managed RAG store: drop in documents (PDF, Markdown, Word, JSON, plain text - and scanned PDFs or images, t…
When building Agent-to-Agent (A2A) systems, the biggest trap is treating AI agents like standard REST APIs. If your Orchestrator Agent synchronously…
Для тех, кто в блоге впервые: здесь я рассказывал про AI ready инфраструктуру, здесь — про её «железную» инженерную основу. После статьи про информа…
Originally published at ai.bedvibe.studio . My portfolio RAG agent holds 1,003 indexed chunks. A question retrieves six and answers. Six of 1,003 is…
There's a hole in almost every AI memory library, and once you see it you can't unsee it: the model that hallucinates is also the thing you've put i…
A team that produces regulatory documents kept getting the same kind of question from other teams: does the current rule allow X? Answering meant so…
The Senior Engineer’s Decision Framework: RAG vs. Fine-Tuning For many engineering teams, the journey into Large Language Models (LLMs) often begins…
I wrote this on X a few weeks ago: I just had a very bad reminder as to the fact these LLMs are statistical parrots, I let it write code I normally…
Many LLM workloads are classification tasks. This can get expensive, and I believe it is going to become more and more important, especially with th…
From Demo to Production: The Guardrails That Make an AI Agent Safe to Ship Hook: Most "AI agents" you see on the internet are demos. Here's the sing…
If your mental model of RAG is "chunk → embed → search → LLM," you're missing about 80% of what actually makes a RAG system production-ready. Here's…
I spent two months building Vestibule, an open-source Python framework for the boring layer of RAG ingestion — stable document IDs, a state ledger,…
What's the difference? An AI agent is a loop-driven system that can decide which tool to call next, keep state across interactions, and adapt its be…
If you are building AI-powered test automation, you may eventually run into this question: Should we use RAG or MCP? The question sounds reasonable,…
2.6 Why Retrieval Is Necessary A Large Language Model only knows what was available during its training. If the user asks about something that chang…
"I fixed a retrieval bug from part 1 with a noise filter and reranking, then found something far more interesting hiding underneath it: a real promp…
RAG projects have a way of collecting infrastructure before they collect evidence. A database gets provisioned. A vector store appears. Then Redis,…
The first three parts of this series covered why production RAG systems fail and how the quality of the data foundation directly affects everything…