AI agents, RAG, and integrations — notes from production.

General

Picking a Confidence Threshold: When Should AI Escalate to a Human?

An AI confidence threshold decides when the system acts alone and when it hands off: two cutoffs not one, calibrate the score first, and start conservative.

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Shadow Mode: Testing an AI Component Against Production Without Touching Decisions

Shadow mode runs a new AI component on live traffic with its outputs changing nothing - logged beside the real decision so you learn if it agrees with reality.

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How to Write an Evaluation Set for an AI Feature Before You Build It

An AI evaluation set is your definition of "working": real inputs from your own data, expected outputs, and the hard cases. Write it before the build.

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When to Use an LLM and When a Classic ML Model Still Wins

LLM vs traditional machine learning is a fit question: where tree models and small classifiers still win on speed, cost, and accuracy - and where they don't.

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What "Grounded AI" Means in Practice — and the Risk of Hallucinated Capability

Grounded AI means answers and actions tie back to real data and APIs, and the system fails visibly instead of inventing. What that takes and how to test for it.

General

AI Agents vs RAG vs Fine-Tuning: What Each One Actually Solves

AI agents, RAG, and fine-tuning solve three different problems - actions, grounding answers in your data, and format or cost. How to tell which one you need.