RAG & Vector Search

The request I hear most often this year sounds the same every time: “We want a bot that answers from our documents and does not make… Continue reading on Medium

Semantic search is one of the most useful parts of RAG. A user does not need to ask a question using the exact same words that appear in a… Continue reading on

How I rebuilt my retrieval system from a static fetch-and-stuff pipeline into a four-agent reasoning loop that actually answers the… Continue reading on Medium

I built four AI retrieval architectures on a laptop and benchmarked them against the same set of documents and questions. Here’s what the… Continue reading on M

Enterprise Document Intelligence [Vol.1 #B2] - The FAQ inverts every brick of the standard RAG pipeline. Parsing is trivial, retrieval doubles as a cache, and f

The right components can make your AI pipeline easier to build, test, and maintain. Continue reading on Python in Plain English »

Most Retrieval-Augmented Generation (RAG) tutorials stop at a naive vector lookup: embed text with OpenAI, store it in Pinecone or Chroma, and perform cosine si

We lost $47,000 in a single week to a production RAG pipeline that answered customer support tickets with high confidence while… Continue reading on Medium »

We lost $47,000 in a single week to a production RAG pipeline that answered customer support tickets with high confidence while… Continue reading on Medium »

For RAG, a lot of us begin with the same almost invisible premise: chunk the document, embed the chunks, retrieve the top matches, and the… Continue reading on

Every RAG pipeline has the same boring first step: turn a stack of PDFs, scans, and slide decks into text an embedding model can actually use. It's also where m

RAG was supposed to fix the knowledge-cutoff problem. Instead, most RAG deployments moved it: the model no longer stalls at its training… Continue reading on Me

When I first built a RAG application, everything looked almost too easy. Continue reading on Medium »

Part 1 of 3 — Enriching RAG with a Knowledge Graph Part 2 covers Agentic RAG. Part 3 covers evaluation and feedback loops. Continue reading on The Bayshore Blue

Vanna proved you can get surprisingly far by retrieving similar queries and letting the model pattern-match. Then someone asks a question that resembles nothing

When we talk about RAG (Retrieval-Augmented Generation), we often focus on embeddings, vector databases, and semantic search. Continue reading on Medium »

The bug wasn’t missing infrastructure. It was a search score that can’t leave its own index. Continue reading on Medium »

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 a practical

Complete RAG Engineering Series, Part 4 | A practical guide to fixed-size, semantic, parent-child, proposition, late chunking, contextual… Continue reading on M

A retrieval system can return highly relevant context and still produce the wrong enterprise answer. The retrieved policy may be obsolete. The document may belo

A few months ago, a friend of mine interviewed for an algorithm engineering position at a large technology company. Continue reading on Medium »

Vector embeddings encode meaning, not exact strings — and identifiers don’t have meaning. Continue reading on Medium »

Retrieval-augmented generation is the most requested AI feature and the most commonly botched. The failure is almost never the generation step -- it's retrieval

A production RAG system can pass every operational check and still fail the only test that matters: telling the truth. Requests complete… Continue reading on Me

Your RAG system is making the same mistake on every query. Not a bad mistake. A fixed one. It retrieves the same number of documents using the same strategy for

A priceless diamond is stolen from a moving luxury train in broad daylight. You have one hour to find it. How do you catch the thief? 🚂💎 If you use Traditiona

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How I Built a NIST AI RMF-Compliant Production RAG System By Lakshman Pandey | August 2026 Introduction I shipped a production RAG (retrieval-augmented generati

Retrieval-Augmented Generation (RAG) is quickly becoming the standard way businesses build AI. By allowing Large Language Models (LLMs) to… Continue reading on

No artigo anterior desta série, separei ETL, chunking e embedding como três camadas distintas de um pipeline de RAG. Mas ter as três bem implementadas não signi

"We have memory. We're using RAG." You have retrieval. Those aren't the same thing, and the gap between them is where agents quietly go wrong. Day 2. RAG finds

Todo problema de RAG parece problema do modelo. Quase nunca é. Demorei pra aceitar isso. Ficava trocando modelo de embedding, mexendo no prompt, achando que o C

Enterprise Document Intelligence [Vol.1 #9ter] - The pipeline from Article 9 calls a model at several steps to be sure it is right. On easy questions that is ne

Every recorded meeting your organization has ever held is already a knowledge base. It just happens to be stored in the least queryable format imaginable, which

How I turned a stack of legal PDFs into a chatbot that cites the exact section of the law, and why the evaluation scores, not the demo… Continue reading on Towa

OmniGraph Publication Series: Continue reading on Medium »

Hey dev community. Ever stared at your chat logs knowing you need to remember context but feeling stuck with simple keyword matching? That is exactly where BizN

Best Vector Database in 2026: pgvector vs Pinecone vs Qdrant vs Milvus The best vector database in 2026 depends on one number: how many vectors you'll actually

The first version answered the question perfectly. Continue reading on Medium »

If retrieval is imperfect and the window is enormous, why not retrieve fifty chunks instead of five and let the model sort it out? Because recall and cost do no

Enterprise Document Intelligence [Vol.1 #12] - The category of question most RAG pipelines silently fail on, and the pipeline shape that handles them The post L

Enterprise Document Intelligence [Vol.1 #11] - When the first answer points elsewhere in the document, the pipeline loops back to fetch the linked context The p

Architecture decisions, API orchestration, and failure handling for a human-feedback ingestion workflow Continue reading on Medium »

Day 100 in production isn't really about chunking strategies anymore.

Enterprise Document Intelligence [Vol.1 #5octies] - Rules propose, LLM validates: six deterministic signals on span-level typography surface heading candidates,

Originally published on FreeDevKit . Retrieval Augmented Generation (RAG) architecture is a critical paradigm for developers seeking to enhance the accuracy, re

RAG Pipeline Optimization Techniques The following techniques are commonly used to optimize a RAG pipeline: Semantic Caching Query Transformation and Expansion

When people build RAG systems, they usually focus on: Continue reading on Medium »