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. 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 ...
Designing a Persistent Knowledge Layer That Refuses to Guess
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 Lay...
Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming
Two open problems, exact-arithmetic checking and a proof assistant, over a single weekend.
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RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop
Enterprise Document Intelligence [Vol.1 #13] - Putting the patterns together, and why this is what “agentic RAG” should look like
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A preprocessing pipeline let my car price model peek at the test set before the exam, and the twelve points of R squared it cheated its way to
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Can a language model do live adversarial level design? Yes, emphasis on the adversarial part
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NVIDIA Nemotron 3.5 Lightning: The AI Agent Workhorse
Long-running AI agents often spend most of their time on routine execution rather than difficult reasoning. After making a plan, they may perform hundreds of tool calls, file reads, validations, commands, and formatting steps, so using a frontier reasoning model for every action can become unnecessa...
Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model
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 RA...
How Baidu Unlimited-OCR Works: Solving Long-Document Transcription
About a month ago, Baidu (often called the “Google of China”) introduced Unlimited-OCR, an advancement over DeepSeek OCR. The model was designed to transcribe long, multi-page documents with high accuracy while delivering fast and stable inference. Unlike conventional vision-language OCR systems, Un...
An operating system (OS) is the fundamental software that acts as an intermediary between computer hardware and user applications. It manages hardware resources such as the CPU, memory, storage, and input/output devices, while providing essential services like process scheduling, file management, se...
Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From
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 Agenti...
Image inputs and structured outputs with Gemma 4 and Ollama
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7 Async Patterns for Running Agents Concurrently in Python
In this article, you will learn seven async patterns for running AI agents concurrently in Python, what each pattern is suited for, and the production-level...
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.
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New Free eBook: Understanding Agentic AI, an Executive Briefing
Written for CEOs, CTOs, CIOs, and technology executives, our new free ebook "Understanding Agentic AI: An Executive Briefing" walks through the components every real agentic system is built from.
Building an Agent-Ready Data Warehouse: What Traditional Architectures Do Wrong
Giving an AI agent access to a data warehouse doesn't automatically make it agent-ready. The real challenge lies in teaching the agent what the data means and when it's reliable enough to use.
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I Thought Loading Data Was the Finish Line. It Was the Starting Point.
Building my first dbt models and learning what “analysis-ready” data actually means
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Building Trustworthy Snowflake AI Agents with Semantic Governance
This year, many data teams have added AI agents to their roadmaps. The excitement is real: an agent that turns a two-day analysis into a two-minute conversation can change how analysts and business teams work together. But agents are only as reliable as the data foundation beneath them. Point them a...
Building a production-ready web interface for a stateful LangGraph agent
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