AI & Data hub
AI & Data: Applied AI, Machine Learning, and Production Data Systems
A hub for teams putting AI into production — LLM integrations, retrieval-augmented generation, AI agents, and the data infrastructure that keeps them honest. Our focus is on the engineering reality after the demo: evaluation suites, guardrails, cost control, and the architectural choices that decide whether an AI feature earns its keep or quietly regresses in month three.
Expect practitioner writing on model selection and hybrid stacks, AI agents, RAG and vector search, prompt engineering, fine-tuning versus prompting, MLOps and LLMOps, drift and regression detection, and the data pipelines underneath all of it. We also publish on where classical machine learning still beats LLMs, when workflow automation solves the problem without a model at all, and the failure patterns we see most often in AI projects we inherit from other teams.
LLMs in Production: Selection, Evaluation, and Guardrails
Most AI features fail at evaluation, not at the model layer. Teams ship a prompt that looks right in a demo, skip the offline eval suite, and find out about the regression from a support ticket. Our approach is the opposite: we pick models to fit the task — frontier hosted models for reasoning-heavy work, smaller open-weight models for extraction and classification — and we build an evaluation harness before the feature leaves a branch. In this section we write about generative AI model selection, prompt and context design, RAG over real-world sources (messy PDFs, SharePoint, Confluence), guardrails and PII handling, and the cost and latency trade-offs that decide whether an LLM feature is viable at the traffic you actually get.
AI & Data Articles
31.07.2026
JSON Render vs MDMA: Why the MD Format Might Be the Better Choice for Generative UI
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Under every reliable AI system is a data system that rarely gets enough attention. This section covers the unglamorous layer — ingestion from heterogenous sources, cleaning and normalization, labeling strategies when you cannot afford a full labeled dataset, embeddings and vector indexes, and the monitoring stack that catches drift before your users do. We also write about MLOps and LLMOps as a practice rather than a vendor list: versioning prompts and datasets alongside code, canarying model changes, shadow traffic for regression testing, and the honest question of when a feature should not be built as ML at all because a rule-based approach is cheaper, faster, and fully explainable.
Most AI projects we inherit from other teams did not fail at the model layer. They failed at evaluation. Somebody wrote a clever prompt, it looked convincing in a demo, and six weeks later the product team is debugging regressions through screenshots in Slack. We refuse to ship an LLM feature without an offline eval suite and a feedback loop wired into the product — that discipline is what separates an AI feature that compounds in value from one that quietly erodes trust until it gets turned off.
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