Prompt

Notes on AI

What AI Receives When You Send a Prompt

A Prompt is commonly misunderstood as the sole input to an AI model. In reality, it is only the visible "Top Slice" of a larger input stack, best understood as a Prompt Sandwich.

Notes on AI

Is AI a Student or an Actor

AI outputs are completions — continuations of patterns, not fact-checked answers retrieved from a database. Like an improv actor following the "yes, and" rule, the model streams tokens to maintain the flow and never stops the scene. Modern models are trained to play the role of a truthful, helpful assistant, but the underlying mechanism is the same: predicting what comes next — and the model is performing truthfulness, not accessing it.

Notes on AI

What AI Is Good At vs Bad At

This episode introduces a practical framework for understanding when to use AI by framing it as a "special employee" with three distinct characteristics.

Notes on AI

The 5-Sentence Mental Model of GenAI

This episode provides a checkpoint after the foundational episodes, compressing the key concepts into five memorable sentences that serve as a mental compass for AI.

Notes on AI

Context Window

The context window is the model's working space, not its memory — only what is currently visible can be reasoned about. Think of it as a desk: only the papers currently on it can be used, and as new papers arrive, old ones slide off the edge. This explains why instructions seem to disappear, why answers contradict earlier statements, and why long conversations slowly fall apart — the model isn't being careless, it simply no longer sees what you think it should remember.

Notes on AI

Context Engineering

Context engineering is the practice of shaping the entire information environment the model operates in — not just writing better prompts. The model never sees just your prompt: it sees system instructions, safety policies, retrieved documents, tool outputs, and your message all at once. Managing what is visible, what is repeated, and what is emphasised matters more than clever phrasing, because the model responds to what it can see — not to what you intended.

Notes on AI

Why Long Chats Get Confused

The LLM inside a chatbot has no memory — every response is generated fresh from the same frozen model. What feels like memory is the surrounding system assembling a prompt from conversation history and re-sending the whole thing each time; the model re-reads, it doesn't remember. When conversations grow too long for the context window, a different approach is used: retrieval-augmented generation (RAG) finds only the relevant pieces and adds them to the prompt at the moment they're needed.

Notes on AI

How to Keep AI Sharp

AI output quality doesn't depend on which model you're using — the model is a fixed variable. The variable you control is the context: what you give the model to read. Context degrades in three ways — accumulation of irrelevant old messages, dilution when earlier instructions compete with new ones, and noise from errors or contradictions in your documents. The fix for each is simple: start fresh, include only what's relevant, and check what you're handing the model before you ask.