#retrieval-augmented-gen

Researching RAG for Dictated Clinical Text

The starting point was talking with my cousin, a provider, who mentioned wanting to treat their own diagnostic notes as something closer to…

Memory You Can Walk Through: OpenViking Turns Agent Context Into a Filesystem

How treating memories, resources, and skills as browsable paths replaces the vector search black box

Finding the Needle in the Chat History: Inside Telegram Search, a Privacy Minded Backup and…

How Tokenization, Vector Embeddings, and a Local First CLI Combine to Make Every Message Findable, in Any Language

🚀 Running RAGatouille with ColBERT v2.0 on Windows: Problems I Faced and How I Fixed Them

If you are building a RAG (Retrieval-Augmented Generation) application, you have probably heard of ColBERT.

Why Most RAG Systems Fail And How to Actually Evaluate & Fix Them

A practical look at why basic retrieval is usually the bottleneck, how to improve both retrieval and generation, and how to properly…

Do We Need to Fine-tune Every LLM?

Learn when to use RAG, LoRA, or full fine-tuning for LLM applications, with code, benchmarks, failure modes, and a practical decision…

Vectorless RAG Won’t Save Your Agent. But It Reveals What’s Actually Broken.

A new retrieval approach claims 98.7% accuracy by throwing out vector databases entirely. The number is real, the idea is genuinely good —…

Enhanced Table Extraction in PyMuPDF4LLM 1.28

1.28 includes a major upgrade to table extraction

RAG vs. Graph RAG: The Great Knowledge Retrieval Debate

Or: Why your RAG system needs a map, not just a compass

AI is Re-Architecting Brand Design

AI Is Re-Architecting Brand Design

Milvus’ta İndeksleme: FLAT, IVF, HNSW ve DiskANN’e Giriş

Merak ettiğiniz dokümanları tarayıp sizinle sorularınızın cevaplarını verip tarışabileceğiniz bir ajan tasarladınız, ilk birkaç bin…

Google’s Open Knowledge Format (OKF): The Markdown Standard Every AI Engineer Should Know About

Google just quietly formalized a pattern most AI teams were already reinventing on their own — and it might save you months of integration…

Enterprise Document Intelligence: a series on building RAG brick by brick, from minimal to corpus…

For AI engineers who want to understand every step, not just call the library

The Retriever Doesn’t Know What Time It Is

Ask a standard RAG system “who is the CEO of Twitter?” and it will hand you back whatever answer is most semantically dominant in its…

Don’t Put Everything into RAG: Building a Safe Candidate Layer for Web Sources

Instead of feeding information found on the internet directly into RAG, first put it through the cache, review, and export steps

A production RAG pipeline for real-world PDFs: structural retrieval, typed answers, cited lines

The four bricks, run end-to-end on a real 45-page car-insurance policy. One surprising coverage question, answered with a number and the…

MinerU: The Open Source Document Parsing Engine That Fixes the Foundation of Every Broken RAG…

There is a common pattern in teams that build retrieval-augmented generation systems and find that their answers are inconsistent…

The Second Brain They Can’t Subpoena: Local RAG on a Pi 5

If your memory is hosted, your thoughts are leased.

AgenticRAG: Letting LLMs Hunt for Evidence, Not Just Answer

The most interesting questions at work are usually the ones that make you open five tabs, skim three documents, and rethink your search…

Before Scaling RAG: Ensuring Technical Terms Are Safe

Transitioning to a local glossary guard, regression tests, and an agentic web ingestion design to reduce local LLM predictions

What Is RAG? The Story Behind Retrieval-Augmented Generation and Why It Changed AI Forever

Part 1 of the “Complete Guide to Retrieval-Augmented Generation (RAG)” series

Pourquoi fait-on confiance à un moteur de recherche et moins à un LLM ?

On tape une question dans Google. On parcourt les résultats, on clique sur l’un d’eux, on lit et on décide soi-même si on y croit. Le…

Making Native Semantic Search Cheaper to Run in ODC

A Question After the Talk

Markdown in PyMuPDF 1.28

1.28 brings native Markdown support and CSS‑controlled PDF generation

LightRAG, hands-on: fast graph-RAG with GPT-5 and Qdrant

LightRAG turns your documents into a knowledge graph and answers questions with a dual-level retrieval paradigm — comprehensive like…

Text embeddings aren’t enough for similarity joins

Article 2 of 3 in the Similarity Joins series

Chapter 3: The Execution Components of an Agent — Repurposing RAG, MCP, and CLI Under the Harness

Rethinking AI: From Tools to Autonomous Agents

When Search Can See: Building Native Semantic Image Search in ODC

Most of the RAG stack I have been building inside ODC starts with the same assumption. The content is text. Chunk it, embed it, store it…

The 5 RAG Architectures and Exactly When to Use Each One in Production

A deep, practical breakdown of every retrieval pattern worth knowing, with LangGraph and LlamaIndex.

Goodbye Vector DB: Building Smarter RAG with PageIndex

How to build a Retrieval-Augmented Generation pipeline based on reasoning — without a single vector similarity operation

KohakuRAG: Climb the Document Tree, Find the Right Evidence

If you’ve ever built a RAG pipeline at work, you probably know the feeling: retrieval looks fine in demos, then quietly falls apart on real…

When Cosine Distance Is Not Enough: Agentic Chunking Inside ODC

Where Level 4 Leaves Off

【RAG 進階之路-2】如何 Monitor BM25？

從錯誤可能性出發，建立 Keyword Search 的 production observability

How Retrieval Systems Power Modern RAG Applications

How modern AI systems overcome hallucinations and outdated knowledge

Hindsight Teaches AI Agents to Learn From Experience, Not Just Remember What Was Said

Benchmarking RAG Architectures Locally on a Real Financial PDF — Part 3: The Measurement Problem

Part 3 of a three-part series. Part 1 covered the setup and the text-retrieval methods; Part 2 covered graph and visual retrieval.

RAG Without the Guesswork: A Standardized LangGraph + LlamaIndex Pattern.

LangGraph + LlamaIndex: Giving Your Agent Real Knowledge with RAG

Building my own LLM-Wiki Research Team

LLM Wikis and knowledge organization

What Actually Happens When Your Prompt Exceeds the Context Window

Large language models feel like they remember everything.

Benchmarking RAG Architectures Locally on a Real Financial PDF — Part 2: Escaping the Text Layer

Part 2 of a three-part series. Part 1 covered the document, the extraction, the evaluation setup, and the text-retrieval methods.

Benchmarking RAG Architectures Locally on a Real Financial PDF

Part 1 of a three-part series.

You Probably Don’t Need a Vector Database — Grep Might Be Good Enough

Plain text search vs BM25 vs embeddings: where each one wins (and where it wastes your time)

RAG Architecture: How LLMs Learn to Look Things Up

Or: The world’s most sophisticated Ctrl+F

For RAG on enterprise PDFs, structure beats fixed-size chunking

Reading the canonical chunking-strategy survey, and showing what each method actually does on a real PDF.

Stream RAG: Building Instant and Accurate Spoken Dialogue Systems with Streaming Tool Usage

1. From conventional language models to real-time spoken intelligence

The Real Problem After RAG: Proving That AI Is Looking in the Right Place

How did I develop natural language navigation, Turkish search normalization, and command routing control in my Telegram-based local AI…

HyperRAG: From Broken Triples to Complete Relational Reasoning

If you’ve ever built a RAG system that looked smart in demos but got strangely lost on multi-hop questions, have you considered this idea…

OWL: The Language That Was Right About Everything and Still Didn’t Win

Semantic Layer

Query Transformation for RAG: Rewrite, Expand, and Decompose Questions So Retrieval Finds What…

Last week we filtered irrelevant documents out before search even ran using metadata tags. This week we fix the other side of the retrieval…

Your RAG pipeline has a stale index problem, Fix it with FolderWatcher in minutes

How to Automatically Reindex Documents in Python RAG Pipelines Using Watchdog and FolderWatcher