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Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1d ago

FAQ as RAG: When You Get to Design the Corpus

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

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 2w ago

Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model

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

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 3w ago

Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One

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

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 3w ago

Loop Engineering for Cross-References: When RAG Answers ‘see Section 7.2’ Instead of the Actual Answer

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

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 3w ago

Building Document Structure with Loop Engineering: Recovering a PDF’s Outline from Body Typography for RAG

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

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

How I Reproduced BM25, Dense Retrieval, and SPLADE on a 16GB MacBook

A practical reproduction of three retrieval baselines, including the crashes, fixes, and score checks that matter for RAG systems. The post How I Reproduced BM2

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract

Enterprise Document Intelligence [Vol.1 #8ter] - Naming the RAG error correctly matters: model reads the context, so a wrong answer is an extraction error, not

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

Prompt Engineering Isn’t Enough: How Four Bricks of Context Engineering Stop RAG Hallucinations

Enterprise Document Intelligence [Vol.1 #9bis] - Your RAG isn’t hallucinating, it’s answering the wrong context faithfully. On real NIST and World Bank document

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval

Enterprise Document Intelligence [Vol.1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering. On the question side, the loop is sm

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited

Enterprise Document Intelligence [Vol.1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation

Enterprise Document Intelligence [Vol.1 #6quater] - Question parsing takes one messy string and writes four typed pieces, each read by a different downstream ca

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

Building Trustworthy Production RAG Systems Through Continuous Evaluation

A practical guide to building an evaluation workflow that catches retrieval failures, hallucinations, and performance drift before they reach users The post Bui

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent

Enterprise Document Intelligence [Vol.1 #7quinquies] - Hallucination is usually garbage-in. Fix retrieval, and the model has nothing left to make up The post Mo

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers

Enterprise Document Intelligence [Vol.1 #9A] - Same paper, same question as Article 1. One upgraded contract per brick: document parsing, question parsing, retr

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

Proxy-Pointer RAG: Temporal Reasoning Without Semantic Precompilation

A technical comparison of Proxy-Pointer and LLM-Wiki The post Proxy-Pointer RAG: Temporal Reasoning Without Semantic Precompilation appeared first on Towards Da

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

Validating the RAG Answer Before the User Sees It: Spans, Quotes, and the Feedback Loop

Enterprise Document Intelligence [Vol.1 #8C] - Structured output is the start of validation, not the end: check the evidence, accept not-found, loop the feedbac

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 1mo ago

Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs

Enterprise Document Intelligence [Vol.1 #8B] - A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typ

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 2mo ago

The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation

Enterprise Document Intelligence [Vol.1 #7ter] - Six positions on the retrieval brick that contradict the cosine-first reflex of mainstream RAG The post The Unt

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 2mo ago

The Untaught Lessons of RAG Question Parsing: Structure Before You Search

Enterprise Document Intelligence [Vol.1 #6ter] - Six positions on the question-parsing brick that contradict the mainstream RAG playbook The post The Untaught L

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 2mo ago

Water Cooler Small Talk, Ep. 11: Overfitting in RAG evaluation

Why memorizing for the exam doesn't mean you understand the subject The post Water Cooler Small Talk, Ep. 11: Overfitting in RAG evaluation appeared first on To

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 2mo ago

Amplify the Expert: A Philosophy for Building Enterprise RAG

Enterprise Document Intelligence [Vol.1 #M1] - The thesis behind every architectural choice in this series The post Amplify the Expert: A Philosophy for Buildin

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 2mo ago

Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG

Enterprise Document Intelligence [Vol.1 #7A] - Stop searching strings. Filter line_df and toc_df. Pick anchors small, expand context large The post Retrieval Is

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 2mo ago

When RAG Users Ask Vague Questions: Clarify Once, Learn the Default

Enterprise Document Intelligence [Vol.1 #6bis] - Ask one focused clarification, learn the default from the answer, stay silent next time The post When RAG Users

Towards Data Science 🔍 RAG & Vector Search ⚡ AI Lesson 2mo ago

Reconstructing the Table of Contents a PDF Forgot to Ship, So RAG Can Scope by Section

Enterprise Document Intelligence [Vol.1 #5septies] - When a PDF prints a contents page but exposes no outline, two ways to turn it back into structure, plus the