All field notes

Diffusion models are the tech behind AI image generation. What they are, how they work at a high level, and where they create business value.

A knowledge graph maps how things relate. What it is, why it matters, and how pairing it with LLMs can make AI answers more accurate and explainable.

Function calling lets an LLM do more than talk—it can call tools, fetch data, and take actions. What it is, and why it's the bridge to real AI agents.

MCP is an open standard for connecting AI to your tools and data. What it is, why a standard matters, and how it simplifies building integrated AI.

Guardrails keep AI inside safe, useful boundaries. What they are, the kinds that matter, and why no production AI should run without them.

Transfer learning lets you build on what a model already knows. What it is, why it slashes data needs, and why nearly all modern AI relies on it.

Few-shot learning teaches an LLM by example—right in the prompt. What it is, when it beats zero-shot, and when to reach for fine-tuning instead.

Zero-shot learning is when an LLM does a task it was never explicitly trained for—just from instructions. What it is, why it works, and its limits.

Chain-of-thought prompting asks a model to 'think step by step'—and it measurably improves reasoning. What it is, why it works, and its trade-offs.

Prompt injection is the AI equivalent of an injection attack—hijacking a model through crafted input. What it is, why it's dangerous, and how to defend.

Distillation shrinks a big model into a small, fast one that keeps most of the smarts. What it is, why it cuts cost, and where it fits.

Quantization makes AI models lighter by using lower-precision numbers. What it is, why it speeds up and shrinks models, and what you trade for it.

Models don't stay accurate forever—the world shifts under them. What model drift is, why it's dangerous, and how monitoring keeps AI reliable.

Inference is what happens every time an AI answers—and it's the cost that never stops. What it is, why it drives economics, and how to optimize it.

An AI model is the learned core of any AI system—but it's not the whole thing. What a model is, the main types, and why the system around it matters more.

Generative AI creates new content rather than just analyzing it. What it is, how it differs from predictive AI, and where it actually pays off.

Training data is what an AI model learns from—and its quality caps the model's quality. What it is, why 'garbage in, garbage out' rules AI, and how to get it right.

Edge AI runs models on the device, not in the cloud. What it is, why it helps with latency and privacy, and what you trade for it.

AI alignment is about making AI do what we actually want—safely and as intended. What it means, why it matters, and the practical version in your product.

MLOps is what keeps machine learning working after launch. What it is, what it covers, and why models fail in production without it.

Data labeling is the unglamorous work that makes supervised AI possible. What it is, why label quality caps model quality, and how to do it right.

Model deployment is where most AI projects stall. What it means to deploy a model, why it's harder than modeling, and what reliable serving requires.

An AI pipeline turns one-off model work into a repeatable, reliable flow. What it is, what stages it includes, and why it's key to scaling AI.

Synthetic data is artificially generated data used to train AI. What it is, when it helps with scarcity or privacy, and where its limits lie.