Enterprise Document Intelligence [Vol.1 #B4] - A diagnostic and five composable operations, not a decision tree

The post Tables in PDFs for RAG: Don’t Flatten the Grid appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B3] - A confident wrong answer is a bug. A bare “no answer” with no justification is almost as bad. Each of the four bricks has one piece of evidence to show

The post A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence appeared first on Towards Data Science.

What each one solves, and runnable code you can paste into a notebook.

The post 5 AI Skills That Will Keep Data Scientists Relevant in 2027 appeared first on Towards Data Science.

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 few-shot prompting becomes a retrieval problem too

The post FAQ as RAG: When You Get to Design the Corpus appeared first on Towards Data Science.

A framework for building RAG pipelines that introduces complexity in response to observed failure modes, from lexical and hybrid search to reranking and agentic information seeking

The post Why RAG Complexity Should Be Earned appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B1] - Three sources of one problem. User typos, fast-typing transcription noise, OCR character errors. Classical spell-check handles one of them. Embeddings carry the rest

The post Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #B00] - Retrieval answers one kind of question. Classifying a request, matching free text to a reference list, reading a table, cleaning OCR noise: each has a cheaper method that works, and the engineering is knowing which one to reach for

The post RAG Is Not the Whole Toolkit: The NLP Techniques Real Problems Still Need appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #2D] - What data scientists say when asked, what the model actually does under the hood, and why the honest answer changes your architecture decisions in enterprise RAG

The post How Does a RAG Reranker Really Work? appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #14C] - One hour with two people, six to ten fields, and the two signals that separate a real column from one that will break a filter later

The post One Document Type, a Million Files: Structured Extraction into the SQL Table RAG Queries appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #M3] - The ten positions the series argues from, and the map of every article that argues them

The post 10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong appeared first on Towards Data Science.

A lightweight runtime layer that separates instructions, evidence, memory, and tool output before they reach the model

The post AI Agents Don’t Need More Context — They Need Typed Context appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #14D] - The index lists what the case type demands before any folder is opened, and the two questions worth building for are not retrieval questions at all

The post Parse the Folder, Not Just the PDFs: The Relational Tables RAG Needs on a Case File appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #14B] - No shared fields means no index to build. One summary line per file plus each file’s own table of contents, and retrieval routes down two levels

The post Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #7sexies] - The unit of retrieval doesn’t have to be a page or a paragraph. When the corpus carries tables, each body row with its column headers is a chunk in its own right, and it’s often the one row the reader asked about

The post Retrieve One Row from a Table, Not the Whole Table: Row-Level Chunks for RAG appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #14C] - One hour with two people, six to ten fields, and the two signals that separate a real column from one that will break a filter later

The post One Document Type, a Million Files: Structured Extraction into the SQL Table RAG Queries appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #M3] - The ten positions the series argues from, and the map of every article that argues them

The post 10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong appeared first on Towards Data Science.

AI agents don’t just have a context problem—they have a context typing problem. When instructions, memory, retrieved evidence, and tool outputs are flattened into one string, their semantic boundaries can disappear. I built a lightweight, zero-dependency Python runtime that keeps those boundaries explicit, tracks provenance, and rejects invalid context transformations before they reach the model. This article walks through the implementation, tests, and what this approach does—and does not—guarantee.

The post AI Agents Don’t Need More Context — They Need Typed Context appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #14D] - The index lists what the case type demands before any folder is opened, and the two questions worth building for are not retrieval questions at all

The post Parse the Folder, Not Just the PDFs: The Relational Tables RAG Needs on a Case File appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #14B] - No shared fields means no index to build. One summary line per file plus each file’s own table of contents, and retrieval routes down two levels

The post Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #7sexies] - The unit of retrieval doesn’t have to be a page or a paragraph. When the corpus carries tables, each body row with its column headers is a chunk in its own right, and it’s often the one row the reader asked about

The post Retrieve One Row from a Table, Not the Whole Table: Row-Level Chunks for RAG appeared first on Towards Data Science.