Blog

Building a Production Website Chatbot with RAG: An Advanced Engineering Guide

A website chatbot is easy to prototype and difficult to make trustworthy. A text box, a model API, and a streaming response can produce a convincing demonstration in a few hours. A production chatbot has to solve a much larger eng...

• evaluation and experimentation
• large language models
• rag and information retrieval

REST and WebSocket in AI Systems: Designing Request-Response and Real-Time Communication

REST and WebSocket are often presented as competing API choices. That framing is too narrow. They solve different communication problems and frequently belong in the same system. REST is an architectural style commonly implemented...

• ai agents
• large language models
• production ml

Random Forest From First Principles: Bagging, OOB Evaluation, Tuning, and Interpretation

A single decision tree is easy to inspect, but it can be unstable. A small change in the training sample may change an early split, which changes the branches below it and can produce a different prediction. Random Forest addresse...

• classical machine learning
• data analysis and statistics
• evaluation and experimentation

A Churn Model Does Not Tell You Whom to Target: Uplift Modeling with a T-Learner

A churn model can estimate which customers are likely to leave. It cannot, by itself, determine which customers should receive a retention offer. These tasks require different target quantities. Churn prediction estimates an outco...

• causal inference
• classical machine learning
• evaluation and experimentation

Embedding Drift in Retrieval Systems: Why Centroid Shift Is Not Enough

Embedding drift is a quiet failure mode. The application may continue returning results, the vector index may respond within its latency target, and the language model may still produce fluent answers. At the same time, the retrie...

• evaluation and experimentation
• production ml
• rag and information retrieval

Causal Overlap Before Estimation: Diagnosing Support, Trimming, and Estimand Change

Causal inference can fail before an estimator is selected. The problem is not always the regression formula, matching algorithm, weighting method, or model family. It is often whether treated and untreated observations are suffici...

Feature Freshness Is a Model Contract: Point-in-Time Correctness, Availability, and Fallbacks

A feature store is often described as infrastructure for sharing and reusing features. Reuse is valuable, but it is not the complete production contract. A model does not only need a feature value. It needs a value computed from t...

• classical machine learning
• evaluation and experimentation
• production ml

An Uncertainty Budget for LLM Systems: Finding Risk Across Retrieval, Tools, and Generation

An LLM system does not become reliable because the model is fluent. It becomes reliable when weak evidence, incomplete tool results, unsupported claims, and uncertain decisions are detected before they create operational harm. The...

• evaluation and experimentation
• large language models
• rag and information retrieval

Choosing an AI Engineering Stack by Responsibility, Not Tool Popularity

An AI engineering stack should be selected by responsibility, not by tool popularity. The important question is not how many products appear in the architecture. It is whether every component reduces a specific engineering risk or...

• evaluation and experimentation
• production ml
• rag and information retrieval

Explainable AI Systems: Matching Explanations to Decisions, Audiences, and Risk

Explainability is useful when it helps someone make a better decision. It does not need to expose every mathematical operation inside a model. It needs to provide enough evidence for debugging, review, communication, governance, o...

• classical machine learning
• evaluation and experimentation
• production ml