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.
Five failure modes that survive constrained decoding, and why your schema validator will never catch them.
The post Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch 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 #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.
A hands-on guide to defining specialist agents and coordinating their work in the Codex CLI
The post From One Agent to a Team: Understanding Codex Subagents appeared first on Towards Data Science.
Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes
The post Hallucinations, Watermarks, Removers, and a Squeezed Balloon appeared first on Towards Data Science.
Understanding Codex hooks
The post Put Your Own Logic Inside the Codex Agentic Loop appeared first on Towards Data Science.
How DFlash trades spare compute for saved memory bandwidth, and why its gains shrink as concurrency rises
The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash 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.
Turning Codex from an interactive assistant into a programmable automation component
The post Running Codex as a Headless Agent appeared first on Towards Data Science.
Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes
The post Hallucinations, Watermarks, Removers, and a Squeezed Balloon appeared first on Towards Data Science.
The Pew Research Center analyzed nearly half a million English-language web pages for AI-generated content. More than a third of pages published since ChatGPT's launch show signs of machine-written text, and commercial .com sites are ten times more likely to contain AI content than .edu or .gov domains.
The article Pew study confirms sharp rise of AI-written text on the web since ChatGPT's launch appeared first on The Decoder.
Understanding Codex hooks
The post Put Your Own Logic Inside the Codex Agentic Loop 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.
Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acceptance metrics, and show what determines whether speculation pays off.
The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash 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.
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