The Ultimate Quantified Self: Building a Private Health Knowledge Base with RAG (PKM for Health)
We've all been there: staring at a blood test report from three years ago, trying to remember if that "slightly elevated" glucose level was a one-time…
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Hardening an AI coding agent: the failures, and the code that fixed them
At Univoco we build retrieval-augmented assistants over a customer's own documentation. One of them is a coding agent that writes code for a proprieta…
5 Practical RAG Challenges and How to Mitigate Them
Retrieval-Augmented Generation (RAG) sounds simple on paper: embed your documents, retrieve the relevant chunks, stuff them into a prompt, let the LLM…
The memory layer that never calls an LLM: what that buys, and what it costs
Part 4 of **The Answerability Problem , and the one that isn't about abstention. Parts 1–3 argued that the field measures the wrong half and that my o…
“Does your agent know what it doesn’t know?” has no answer. It has a coordinate.
Part 3 of **The Answerability Problem . Part 1 showed the standard harness excluding the questions that test refusal, and my own system scoring 0.000 …
Relevance is not answerability: six signals, and none of them beat plain cosine
Part 2 of **The Answerability Problem . Part 1 showed the standard harness excluding the questions that test refusal, my own system scoring 0.000, and…
Corrective RAG for billing: the bug is not retrieval, it's the model narrating correct numbers wrong
Most RAG demos are graded by an audience that cannot check the answer. Ask a docs bot something, get a fluent paragraph back, nobody in the room knows…
Облачные ИИ не справляются, MiniLM-L6 ломается на философии: строим локальный RAG для сложных семантических текстов
Этот проект долго вынашивался и, в конце концов, начался как очередная попытка разобраться в философских текстах, написанных Джейн Робертс во второй п…
Data, Context & RAG Lineage Governance for Enterprise AI Agents
The RAG Security Gap Retrieval-Augmented Generation (RAG) has rapidly emerged as the foundational architecture for grounding enterprise AI agents in p…
From RAG to Agentic AI. How I Added LangGraph to My Local
In my previous article , I built a fully local RAG assistant Ollama, ChromaDB, LangChain, all running in Docker. It answered technical support questio…
Silent Drift: Why Re-Embedding Only on Count Changes Rots Your Semantic Index
My index reported 354 points. The collection had 451. Both numbers were "correct," and that gap is the whole problem. Here's the setup. I have a wiki …
Why does parsing scientific papers for RAG still break on equations and tables?
If you've tried building RAG over scientific papers, you've probably hit this: the PDF looks fine, the text extracts fine, and then a table with merge…
🚀 From Transformers to AI Agents: The Complete Engineering Guide to Modern AI Architecture (LLMs, RAG, Vector Databases & Agentic Systems)
Most people think ChatGPT is "the AI." In reality, ChatGPT is just one layer of a much larger engineering stack. Modern AI applications aren't powered…
Как я собрал антискам‑бота на грязных данных: детектор, типизатор и грабли по дороге
В первой статье я разбирал RAG-модуль этого проекта и главный вывод — что RAG оказался про замеры, а не про код. Здесь — про то, как устроена вся сист…
Coverage Before Creativity: The RAG Gate That Keeps My Blog Pipeline Honest
The first failure I had to eliminate in the blog pipeline was not a bad paragraph. It was a bad evidence set. The system was finding a few nearby chun…
Where Does RAG Actually Cost You Money? (Episode 2)
Document Extraction I thought I had solved document extraction. My Node.js project could pull text out of a PDF. The library was free. No API bill, no…
How to Build a RAG Pipeline from PDFs Using Python
How to Build a RAG Pipeline from PDFs Using Python Most RAG pipelines don't fail at retrieval. They don't fail at the model either. They fail at inges…
André Dias Moreira Prol: Securely Connecting AI to Internal Docs With RAG
Every company sits on a mountain of internal knowledge — contracts, technical manuals, compliance policies — that traditional AI models simply cannot …
I Was Optimizing Ranking While the Real Problem Was Selection
I mistook movement for improvement. For three months, I changed our ranking algorithm every two weeks. BM25. Hybrid BM25 + vector search. A cross-enco…
Building Dev-Code: An Agentic AI Coding Assistant With RAG Memory and VS Code Integration
I recently completed Dev-Code , an AI coding assistant project built around Agentic AI, RAG-style memory, and developer-tool integration. GitHub repo:…
Chat with Your Documents: Building a RAG Pipeline with AWS Blocks
One of the first features users expect from an AI application is deceptively simple: Upload a document. Ask questions. Get accurate answers. Whether y…
RAG for developers who aren't AI engineers: what actually matters
RAG for developers who aren't AI engineers: what actually matters Most non-AI developers have a mental model of RAG that is either wrong or dangerousl…
Retrieval-Augmented Self-Recall — Part 6: The Fine-Tune That Did Nothing, and Shipping It as an MCP Server
Part 6 (finale) of Retrieval-Augmented Self-Recall. Code: RE-call . Part 5: the gap threshold that didn't transfer . I fine-tuned the embedder on my o…
Leonard Shelby Is a RAG Pipeline
Memento as a two-hour postmortem for retrieval-augmented generation. Leonard Shelby makes the pitch himself: memory is unreliable. It reinterprets, it…
RocheDB: Data Locality as a First-Stage Retrieval Index
One of the ideas behind RocheDB is easy to miss if we describe it only as a NoSQL database, a document store, or a vector-aware storage engine for AI …
Beyond Chatbots: Wrapping My RAG Agent in an MCP Server
In my last post, I walked through a RAG pipeline that answers questions from a company policy document. The next question I wanted to answer: what hap…
Why Not Every AI Application Needs Vector Embeddings
How building an AI chapter generator taught me to stop reaching for a vector database first. When I started building my AI chapter generator, I spent …
Архитектура RAG с виртуальной файловой системой и LLM-роутером
Классический RAG (Retrieval-Augmented Generation) решает задачу поиска релевантного контекста в векторных базах данных. Однако при работе с большими о…
Building a Robust RAG Pipeline Architecture for Production
Answer up front: A RAG pipeline architecture is a set of connected services that ingest raw documents, turn them into embeddings, store them in a vect…
A Folder of Docs Is Not a Knowledge Base
Disclosure up front: I build agentproto and its corpus tooling, which is what the walkthrough uses. The commands are real and checkable; the problem i…