Towards Data Science
2d ago
Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline
Enterprise Document Intelligence [Vol. 1 #13bis] - The four bricks return useful results most of the time.
PANet Paper Walkthrough: When Feature Pyramids Go Bottom-Up
Understanding how PANet shortens the path between low-level and high-level features The post PANet Paper Walkthrough: When Feature Pyramids Go Bottom-Up appeared first on Towards Data Science .
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Towards Data Science
Aug 7
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 Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One appeared first on Towards Data Science .
Towards Data Science
Jul 22
Loop Engineering for RAG Generation: Iterate top-k One at a Time
Enterprise Document Intelligence [Vol. 1 #8bis] - Two regimes for sending retrieved candidates to the generation brick, the sufficiency signal that picks between them, and the per-question type dispatch that makes it cheap The post Loop Engineering for RAG Generation: Iterate top-k One at a Time appeared first on Towards Data Science .
Towards Data Science
Aug 6
Last Month’s Machine Learning Lessons Learned
The downside of conference travel The post Last Month’s Machine Learning Lessons Learned appeared first on Towards Data Science .
Towards Data Science
Aug 3
Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On
Enterprise Document Intelligence [Vol. 1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops).
Towards Data Science
Jun 8
4 New Techniques to Maximize Claude Code
Get the most out of Claude Code with these four techniques The post 4 New Techniques to Maximize Claude Code appeared first on Towards Data Science .
Towards Data Science
Jul 9
Loop Engineering for Hierarchical Retrieval: Reading a Long Document by Its Table of Contents
Enterprise Document Intelligence [Vol. 1 #7quater] - A 492-page document has a 358-entry table of contents.
Towards Data Science
Jun 24
Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End
Enterprise Document Intelligence [Vol. 1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End appeared first on Towards Data Science .
Towards Data Science
Jun 1
It’s the Lessons We Learned Along the Way. Or, Is It?
Research projects in the age of AI The post It’s the Lessons We Learned Along the Way. Or, Is It?
Towards Data Science
Jul 17
A Production RAG Pipeline in Action: 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 The post A Production RAG Pipeline in Action: Every Answer Typed and Cited appeared first on Towards Data Science .
Towards Data Science
Aug 6
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 post Loop Engineering for Cross-References: When RAG Answers ‘see Section 7.
Towards Data Science
Jul 26
How to Efficiently Prompt Claude Code
Maximize your efficiency with Claude Code The post How to Efficiently Prompt Claude Code appeared first on Towards Data Science .