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.
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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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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.
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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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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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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 a production-ready web interface for a stateful LangGraph agent
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My Fall-Detection Model Scored 94%, and It Was Lying to Me
How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on
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I Built an AI Data Agent Which Can Query Data and Answer Business Questions. Here’s How.
A step-by-step guide to building a data agent and conversational interface that let business users to explore data in natural language without SQL
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I Built a Tool-Calling Agent in Python. Here’s How I Debugged It
A minimal loop with real API calls, validation, compact outputs, and trace evidence before adding an agent framework
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Is This Slop? Detecting AI-Generated Content Without a Model
Research-backed cues to detect LLM-generated text along with the mathematical intuition as to 'why'
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Building Document Structure with Loop Engineering: Recovering a PDF’s Outline from Body Typography for RAG
Enterprise Document Intelligence [Vol.1 #5octies] - Rules propose, LLM validates: six deterministic signals on span-level typography surface heading candidates, one bounded loop keeps the real ones, and the same toc_df drops back into the RAG pipeline
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A practical guide to Bronze, Silver and Gold, with a working Python and DuckDB example
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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). Knowing which layer you are standing on is half of build...
How use Claude to craft an outstanding resume that lands offers
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I Replaced a 15-Minute Booking Process with a LangGraph AI Agent
A step-by-step guide to building, running, and monitoring a stateful customer support agent using Python, LangGraph, and Langfuse.
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Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler
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 mi...