Prepare These 5 Assets Before Your AI Agents Take On More Work
How to define recurring work, give AI the right context, explain what high-quality work looks like, and decide where human judgment is still needed.
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Building Trustworthy Production RAG Systems Through Continuous Evaluation
A practical guide to building an evaluation workflow that catches retrieval failures, hallucinations, and performance drift before they reach users
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Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent
Enterprise Document Intelligence [Vol.1 #7quinquies] - Hallucination is usually garbage-in. Fix retrieval, and the model has nothing left to make up
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How Much Does It Actually Cost to Run a Local LLM? (Euros per Million Tokens, Measured)
I measured the actual GPU electricity for eight local models on one RTX 3090 — and the cheapest wasn't the smallest, nor the priciest the biggest.
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Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs
How to stop parsing JSON by hand and start trusting your model's output
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A minimal OpenAI Agents SDK implementation where retrieval becomes a search-read-decide loop
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Building Models in Two Worlds: From Latent Constructs to Behavioral Signals
My PhD models tried to explain why people engage. My industry models predict who will. The statistics barely changed. Everything around them did.
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The Three Dimensions of Custom Agentic Alignment: Purpose, Principles and Practices
A framework for aligning agentic AI with enterprise intent to ensure consistent scenario‑wide autonomous behavior.
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Long Context Isn’t Free — I Built a Safe Prompt-Pruning Layer That Makes LLM Systems Work
LLMs don’t fail because they forget—they fail because they remember too much. As conversations grow, prompts accumulate redundant and low-value tokens, driving up cost and latency while silently degrading output quality. This article introduces a deterministic prompt-pruning layer that reduces token...
I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer
Building a production-ready RSS pipeline with Python, Docker, PostgreSQL, and Kestra
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PySpark for Beginners: Building Intermediate-Level Skills
A practical next step into partitions, shuffles, joins, caching, and execution plans.
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What our over-dependence on external consulting teaches us about delegating our minds to machines
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Map AI value, design workflows, redefine talent, upgrade the executive team, and measure the business impact.
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How to decide when an AI agent should act on its own by using cost asymmetry instead of a fixed confidence cutoff
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Measuring Structure Stability of Econometric Models
The simplest most important idea for time series forecasting
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Proxy-Pointer RAG: Temporal Reasoning Without Semantic Precompilation
A technical comparison of Proxy-Pointer and LLM-Wiki
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Survival Analysis for Data Drift and ML Reliability
Treating model degradation as a time-to-failure problem
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Best-worst comparisons, MaxDiff-style judging, and Plackett-Luce utility scores give agent teams a cleaner way to decide which configs to ship, prune, and route toward next.
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Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs
Enterprise Document Intelligence [Vol.1 #8B] - A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typed LLM call
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PANet Paper Walkthrough: When Feature Pyramids Go Bottom-Up
Understanding how PANet shortens the path between low-level and high-level features
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Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination
Enterprise Document Intelligence [Vol.1 #8A] - The schema is the contract: every field is a question the pipeline asks the model, and every answer is checkable
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