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.

Enterprise Document Intelligence [Vol.1 #14A] - Three questions tell you which shape a document collection has, and each shape wants a different architecture

The post Three Kinds of RAG Corpus, and What It Costs to Build for the Wrong One appeared first on Towards Data Science.

A hands-on guide to fine-tuning LLMs for the real world

The post How to Fine-Tune an LLM: An End-to-End Guide appeared first on Towards Data Science.

A controlled comparison of a top-5 RAG pipeline and a full 127,000 token prompt on the same 12 questions, same system prompt and same model. Graded blind on correctness, completeness and grounding.

The post Kimi K3’s 1M Token Context Window vs. RAG: Cost, Latency and Answer Quality appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #13bis] - The four bricks return useful results most of the time. Loop engineering is what the system does the rest of the time: when retrieval misses, when generation fails the schema, when the listing comes back incomplete, when an API call times out. Three control surfaces (trigger, termination, recovery) and one rule that separates a useful loop from a spinning one

The post Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline appeared first on Towards Data Science.

RAG Retrieves, It Never Remembers. A vendor-neutral blueprint for applications that accumulate understanding. Includes a complete Azure-native implementation (Microsoft Foundry, Azure AI Search, Cosmos DB, FastAPI) mapped to a property-insurance corpus. 

The post Designing a Persistent Knowledge Layer That Refuses to Guess appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #13] - Putting the patterns together, and why this is what “agentic RAG” should look like

The post RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop appeared first on Towards Data Science.

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 needless latency. A per-question signal routes them past the model, about two seconds saved for a keyword match.

The post Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model appeared first on Towards Data Science.