The Lab Notes

Hybrid Search: Two Paths, One Ranking (and Why the Merge Needs Testing)

Keyword search and semantic search fail in different ways. Hybrid search merges both into one ranked list, but only testing shows whether the combination actually helps.

Similar Is Not the Same as Supported: Why Search Results Need Checking

Your search returns the most similar passage, but similar does not mean it answers the question. Learn the checks that turn a match into real supporting evidence.

Evaluate the Judge, Too: Can You Trust an AI Grader?

AI judges can grade thousands of answers, but their scores are not automatically reliable. Learn how to check a model-based grader before you trust it.

Same Scores, Different Decisions: How One Threshold Changes Everything

Your model gives every message a score, but someone still has to decide where the cutoff sits. Learn how one threshold changes alerts, misses and precision.

Prompt Caching Is Not Answer Caching: What Your AI App Is Really Reusing

Prompt caching sounds like saving answers, but it reuses processing, not replies. Learn what gets reused, why exact prefixes matter, and how to measure real savings.

Your Test Set Is Not a Feedback Loop: How to Keep Model Evaluation Honest

Tuned your model using test scores? Then your test set is no longer a fair test. Learn the three roles of your data and how to keep your evaluation honest.

Faster Calls Don't Always Mean a Faster App (Here's How to Tell)

Your model call got faster, but the app still feels slow? Learn where the time really goes, and how to cut cost and latency without losing answer quality.

Design the Problem Before You Choose the Stack

Asked to build an AI assistant for company policies? Don't pick tools yet. Learn the questions to ask first, and how to plan for failures and testing.

Stored Is Not the Same as Remembered: How AI Agents Keep Track

Your agent saved a note, yet it still acted on the wrong thing. Learn the difference between stored information, task state and the context a model actually sees.

Before You Change the Prompt, Read the Agent Trace

Your agent gave a wrong answer, and your first instinct is to rewrite the prompt. Stop there. Learn how to read an agent trace and find the first visible mismatch.

Why Your AI Agent Should Not Obey Every Instruction It Reads

A web page your agent reads can hide instructions meant to redirect it. Learn where this risk enters your system, and why rules in a prompt alone do not stop it.

Your Model Scored 95% Accuracy. Is That Actually Good?

A 95% accuracy score can look impressive, but it may hide a model that misses every important case. Learn how to compare against a simple baseline and pick the right metric.

Do You Need Multiple AI Agents? How to Know Before You Build

Multi-agent systems sound impressive, but they are not always better. Learn five questions to ask, and how to test whether a second agent really earns its place.

Your Agent's Tool Interface Is a Contract

Your agent keeps picking the wrong tool? Learn how to design clear tool names, inputs, results and errors, and test whether the design works.

BM25 vs Vector Search vs Hybrid Search: A Practical Retrieval Guide

BM25 vs vector search vs hybrid search: learn what each one is good at, how RRF combines them, and how to test which fits your RAG app.

A Good Score Can Hide Data Leakage

A high evaluation score can still mislead. Two checks to catch data leakage in preprocessing and features before you trust your model.

How to Debug a RAG System That Retrieves the Wrong Context?

Your RAG app retrieves the wrong context? Find where the pipeline breaks, test each step, and fix retrieval with evidence, not guesswork.

RAG vs. Fine-Tuning: When Should You Use Each?

RAG or fine-tuning? Learn how each works, what each costs, and how to choose the right approach for your data and accuracy needs.

A Prompt is Not a Security Boundary

A prompt can tell an AI agent what not to do — but it can't close a network route, revoke a credential, or reverse a write. Real incidents at OpenAI and Anthropic show why production AI systems need containment, not just instructions.

How Do You Evaluate a RAG System? The 4 Metrics That Actually Matter

How do you know your RAG system works? Learn four metrics, faithfulness, answer relevance, context precision and context recall, and how to diagnose each.

How Fortune 500 Companies Actually Use Machine Learning

Machine Learning isn't just research and algorithms. It's quietly powering the apps and services we use every day. Here's how Amazon, Netflix, Uber, Tesla, and more actually use ML to solve real business problems.

RAG Explained: Why Your LLM Doesn't Actually Know Anything (And How to Fix That)

Your LLM doesn't know your company's data. RAG fixes that without retraining the model. Here's how it works, step by step, in plain English.