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.

How AI has massively changed my day-to-day workflow

The post A Day in the Life of a Data Scientist in 2026 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.

Terabytes worth of credentials, many belonging to the world’s biggest and most sensitive organizations, have been exposed in a supply-chain attack on LiteLLM, an open source tool that streamlines AI-driven software development. Microsoft, Amazon, Cisco, Samsung, and Salesforce are only a handful of the entities whose access secrets were exposed.

The revelation was posted on Tuesday and Wednesday by security firms CloudSEK and Hudson Rock. CloudSEK said it found cloud keys, repository tokens, SSH keys, Kubernetes secrets, package publishing credentials, environment variables, and AI provider keys that could allow attackers to gain access to more than 2,500 organizations.

40 minutes is all it takes

The credentials were extracted during a 40-minute window in March while the victims used compromised versions of LiteLLM downloaded from the package’s official location in the Python Package Index repository. Hudson Rock said it made the discovery after analyzing a 195TB file that it obtained. Neither firm identified the source of the information.

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Enterprise Document Intelligence [Vol.1 #5nonies] - Nature, plan, execute, synthesize: closing brick 1 with a dispatcher that reads each PDF’s nature and picks the method that fits, fitz, Docling, PaddleOCR, EasyOCR, MinerU or Surya, then folds the outputs into one corpus

The post Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From appeared first on Towards Data Science.

I replayed the same 27 real production tasks through two local models, one hardware upgrade apart, to find out what it actually takes to replace Claude as the brain behind a 90-tool personal agent.

The post Can a Local LLM Run My AI Assistant? appeared first on Towards Data Science.

Why use it? How to implement it? What can we do when it fails?

The post How to Implement Structured Output with Local LLMs appeared first on Towards Data Science.

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.

An open, 2.8-trillion-parameter model shipped with 47 pages of its own recipe. Reading it tells you what building a frontier model now involves, and how little of it is the model.

The post How a Frontier Model Gets Built, Read from the Kimi K3 Report appeared first on Towards Data Science.

Research-backed cues to detect LLM-generated text along with the mathematical intuition as to 'why'

The post Is This Slop? Detecting AI-Generated Content Without a Model appeared first on Towards Data Science.

Building manager–specialist workflows with the OpenAI Agents SDK

The post Using Agents as Tools appeared first on Towards Data Science.

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). Knowing which layer you are standing on is half of building and debugging RAG

The post Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On appeared first on Towards Data Science.

How to use Claude to craft an outstanding resume that lands offers

The post How Claude Help Me Build My $200k+ ML Resume appeared first on Towards Data Science.

Most coding agents treat prompt construction like retrieval: gather more files, add more context, hope the model figures it out. But that approach breaks down fast. As context grows, irrelevant code competes for attention, and when the window fills, agents start compressing their own memory—often mid-task. What looks like “forgetting” is usually just degraded context. This article explores a different approach: treating prompt construction like a compiler that decides what to keep, what to reduce, and what to discard entirely.

The post Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler appeared first on Towards Data Science.

A hybrid LLM application pattern that combines a predefined workflow with adaptive agent behavior

The post Put the Agent Inside the Workflow appeared first on Towards Data Science.

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