Prompt Engineering featured
Prompt Engineering Is Mostly Specification Writing
The prompts that work in production don't look like magic spells. They look like the spec a good engineer would hand a new teammate.
a Purdue AI senior's field notes
How modern AI actually gets built.
Prompt & context engineering, agent loops, harnesses, MCP servers, model routing, GPUs, observability, and the foundations underneath. Written by students, read by builders.
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// what you'll find here
const neuralgist = {
foundations: ['ml', 'deep learning', 'models', 'gpus'],
trends: ['prompt eng', 'context eng', 'harnesses', 'loops'],
patterns: ['semantic layer', 'agents', 'model switching'],
tooling: ['mcp', 'workflows', 'observability'],
author: 'purdue ai, class of 2027'
};
// featured
Start here
// latest
Fresh off the notebook
LLM Observability: If You Didn't Trace It, It Didn't Happen
Model calls are non-deterministic, expensive, and slow. The only way to debug, cost-control, and improve them is to record everything. Here's what to log and what to look at.
Workflows vs. Agents: Pick the Boring One First
Most "agent" use cases are workflows in disguise. Knowing the difference saves money, latency, and a lot of debugging.
MCP Servers, Explained Like You're Going to Build One
The Model Context Protocol standardizes how models discover and call tools. Here's the mental model, the three primitives, and a server in forty lines.
Model Switching and Routing: Use the Smallest Model That Works
One model for everything is the expensive default. Routing tasks to different models by difficulty, cost, and latency is how real systems stay affordable.
Chatbots and Agents: The Difference Is Who's Holding the Tools
A chatbot answers. An agent acts. Building either well comes down to state, tools, and knowing when to hand off to a human.
The Semantic Layer Is What Makes "Chat With Your Data" Not Lie
Text-to-SQL demos fall over the moment two people define "revenue" differently. A semantic layer is where you write down what words mean before the model guesses.
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Trends
What the field is arguing about this month.
Patterns
Architectures that keep showing up in real systems.
// watch
Videos worth your time
Intro to Large Language Models — Andrej Karpathy
Let's build GPT: from scratch, in code — Andrej Karpathy
But what is a neural network? — 3Blue1Brown
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