Building a Hybrid RAG System with FAISS, BM25, and Agentic AI
As part of my AI Engineering journey, I recently worked on a project that helped me understand how Retrieval-Augmented Generation (RAG) works in pra…
Последние новости
⚑ Сообщить о проблемеTech news from the best sources
Все темы
AI
Gear
News
Tech
agents
ai
api
architecture
automation
beginners
career
database
devchallenge
devops
javascript
llm
machinelearning
mcp
opensource
performance
productivity
programming
python
react
security
showdev
testing
tutorial
typescript
webdev
Design, Optimization, and Deployment of a Hybrid Agentic Retrieval-Augmented Generation (Agentic-RAG) Architecture
Abstract Traditional RAG pipelines usually work deterministically by using only static vector search[cite: 1]. Because of this, vocabulary mismatche…
Bedrock Guardrails Blocked My RAG Bot's Own Questions: Denied Topics and Contextual Grounding Tested
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…
Mind Discipline: Why Our AI Advisor Only Reads Hand-Crafted Contracts
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…
Is Agent Memory Just RAG With Extra Steps? We Opened the Source Code to Find Out
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…
LLMOps for RAG Systems — Production Checklist
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…
Using SynapCores as a LlamaIndex Vector Store + Property Graph Store
Most LlamaIndex setups end up with two separate backends once you go beyond plain vector search: a vector store for VectorStoreIndex , and a separat…
Vector RAG: Why It’s Winning in Production
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…
Your local RAG isn't slow — it re-reads every document on every question
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…
From Pixels to Prescriptions: Building an AI Pharmacist with YOLOv10 and RAG
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…
Your RAG Demo Works Because Someone Picked the Documents
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…
Your Documents, Chunked and Searchable: The Knowledge Base in ByteChef
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…
Architecting for Reliability: The Role of Message Brokers in Multi-Agent AI
When building Agent-to-Agent (A2A) systems, the biggest trap is treating AI agents like standard REST APIs. If your Orchestrator Agent synchronously…
My Agent Answers From 0.6% of Its Corpus and Reports It Like a Full Read
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…
I Built an MCP Memory Server Where Code, Not the Model, Decides What Gets Stored
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…
Three things I got wrong measuring my own cache
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…
RAG vs. Fine-Tuning: The AI Engineer's Decision Framework
The Senior Engineer’s Decision Framework: RAG vs. Fine-Tuning For many engineering teams, the journey into Large Language Models (LLMs) often begins…
I Almost Shipped a RAG Assistant That Lied About APIs That Don't Exist
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…
Model Cascade: making LLM classification cheaper
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
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…
A Developer's Checklist for Every RAG Lifecycle (Beyond Chunk-Embed-Search)
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…
483 tests passed, but Vestibule RAG framework wasn't installable — lessons from building with AI agents
I spent two months building Vestibule, an open-source Python framework for the boring layer of RAG ingestion — stable document IDs, a state ledger,…
ai agents vs automations: When to build an autonomous agent and when a simple workflow suffices
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…
RAG vs MCP in AI Testing: Stop Treating Them as Competitors
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,…
Chapter 2 (Part 2) Knowledge Retrieval Engine
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…
My RAG Pipeline Got Hijacked by Retrieved Text: An Accidental Prompt Injection
"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…
Local RAG starts with retrieval, not infrastructure
RAG projects have a way of collecting infrastructure before they collect evidence. A database gets provisioned. A vector store appears. Then Redis,…
Scaling RAG Systems: Production Architecture, Performance, and Cost Optimization
The first three parts of this series covered why production RAG systems fail and how the quality of the data foundation directly affects everything…
Building an AI Pharmacist: Detecting Drug-Drug Interactions with RAG and OCR
Ever looked at a pile of medicine bottles and wondered, "Is it actually safe to take these together?" Polypharmacy—the simultaneous use of multiple…
6 Node.js Tenant Guardrails for Cheap Invoice RAG (Count Embeddings and LLM Spend)
Short answer: make every indexing and retrieval decision attributable to a tenant before the Node.js service sends text to an embedding model or an…