Prompt Engineering & Structured Prompting

Learning objectives

Here is what you can do:

1. Write a complete Role–Context–Task–Format (RCTF) prompt for a real analyst task, and diagnose which of the four elements is missing when a prompt underperforms.
2. Use one or more worked examples (few-shot) to teach a model a format, a judgment call, or a house style, and explain when adding examples helps versus when it silently teaches the wrong lesson.
3. Decompose a multi-step analyst task into a chain of smaller, independently verifiable prompts, and explain why chaining reduces but never eliminates compounding error.
4. Write prompts that explicitly demand a cited source, a stated confidence level, and a named list of uncertainties, and explain why asking a model "how confident are you" does not, by itself, produce a trustworthy number.
5. Specify a structured output format (a JSON schema, a fixed-column table) precisely enough that a human or a script can check it mechanically, and handle a field the source does not support without letting the model invent one.
6. Apply the four analyst prompt patterns (extract-a-table, explain-this-filing-note, draft-then-critique, red-team-my-thesis) from memory, each ending in its own verification step.
7. Trace every prompted output back through the Primary-Source Guardrail (Trace, Match, Recompute, Date-check, Cite) before it is used in any real analyst deliverable.
8. Diagnose, from a short transcript alone, which common prompt-engineering mistake (a vague ask, a buried instruction, an unverified confidence figure, a leading question, an unchecked chain) is responsible for a bad output.
9. Build and maintain your own small library of reusable, analyst-grade prompt templates, and state, for each one, the by-hand skill you must still own before trusting it.

Prerequisites & connections

Builds on. AI0.01's mental model (next-token prediction, no built-in notion of true, fluent output that can still be wrong) is the reason every technique here ends in verification rather than trust. AI0.02 gave you the tool-evaluation rubric and the category map (chat assistant, coding copilot, research/agentic, and the rest); the assumption from here on is that you can already place a tool in a category, so the teaching now is what to actually type into it. Like every node in this branch, AI0.03 is independent of the eleven-phase Finance sequence: nothing here gates the accounting, valuation, or industry phases, and nothing outside AI0.01–AI0.02 gates entry here.

Feeds forward. AI0.04 (Retrieval & RAG) reuses the citation discipline built here directly: a grounded answer is only as good as the prompt that demanded the grounding, and "chat with a filing" is, underneath, an extract-a-table and explain-this-filing-note prompt aimed at a retrieval system instead of a single pasted excerpt. AI0.05 (agents) is built entirely out of prompts: every step of a plan-act-observe loop is itself a prompt, and a badly specified step is exactly where an agent starts to compound errors. The chaining discipline you build here is the seam. AI0.06, the branch's crown, assumes you already know what a well-formed prompt looks like, so that when an output is still wrong despite good prompting, you can tell that apart from an output that is wrong because the prompt invited it to be. AI0.07's capstone puts your prompt-pattern library to work across real analyst tasks. Nothing here requires code; nothing here requires a paid tool; every exercise runs in whatever tool you chose in AI0.02.

Why prompting is a real lever, not a courtesy

A chat model is built to be cooperative: given almost any question, it will produce a fluent, confident-sounding answer, and it will rarely volunteer "I don't have enough information to answer that" unless the prompt explicitly invites it to say so. That single fact is why prompt quality matters as much as it does. A bad prompt does not fail loudly. It does not come back blank, or hedge visibly, or flag itself as unreliable. It comes back looking exactly as polished as a good prompt's answer, because fluency and formatting are products of the same next-token-prediction process regardless of whether the underlying content is right (AI0.01's payoff line, showing up here for the first of many times). There is no dashboard warning light distinguishing a well-grounded answer from a plausible improvisation: the only lever you have before the fact is how precisely you ask, and the only check you have after the fact is verification. What follows is entirely about the first half of that pair.