Writes prompts that get reliable results

Concepts · competency concepts/prompts-reliably

Taught in: the Concepts course

Draws on: Prompting, How language models work

Learning objectives

Turns a vague request into instruction, context, example and format (base)

Claim	Why	Example
A prompt separates the instruction (what to do) from the context (what to work on) and marks the boundary between them.	When instruction and material run together, the model treats parts of the material as orders or parts of the order as material.	"Summarize the text between the triple quotes for a sales manager" with the pasted email inside the quotes, rather than the email followed by "summarize".
The prompt names the audience and the purpose of the output.	The same content for a customer and for a colleague differs in length, tone and what may be left out, and the model cannot guess which.	"Write the release note for end users who do not read code; skip internal ticket numbers" replaces "write a release note".
When the wanted output is hard to describe, the prompt shows one example of it.	One good example fixes format, length and tone at once, where a paragraph of rules leaves room for interpretation.	The learner pastes one past meeting summary in the house format and writes "produce the same for these notes".
The prompt states the output format, including length.	Left to its default, the model picks a length and format for a general reader, which is rarely what the next step needs.	"Five bullets, each under 15 words, no introduction" instead of "keep it short".

Served by: Show one example, Instruction, context and format, The prompt before the conversation

Improves a result by changing the prompt, not by retrying (base)

Claim	Why	Example
When the output is wrong, the learner names what is wrong with it and changes the prompt to address that, instead of rerunning the same prompt.	Rerunning draws another sample from the same distribution, and the same gap in the instructions produces the same class of mistake.	The summary skipped the decisions made, so the learner adds "list every decision as its own line" instead of pressing regenerate.
The learner changes one thing at a time and compares the result with the previous one.	Changing five things at once shows whether the total worked and hides which change did it, so the next prompt cannot build on it.	The learner first adds the audience, sees the tone fix, and only then works on the length.
A correction the learner had to make twice moves into the prompt or into a saved template.	A fix typed in the chat is gone with the session, but a fix in the prompt is there for every run.	After twice asking for "dates in ISO format", the learner adds the line to the prompt they keep for the weekly report.

Served by: Change the prompt before you retry

Asks for output in a shape the next step can use (base)

Claim	Why	Example
The learner asks for output in the format the next step consumes: a table, a list of named fields, JSON, or a file in a known layout.	Prose has to be reformatted by hand or by another prompt, and each reformatting is another place for mistakes.	For action items that go into a tracker, the learner asks for "one line per item: owner, task, due date, separated by tabs".
The requested format names its fields and the allowed values as well as its type.	"Return JSON" gives a different set of keys every run, and naming the keys makes runs comparable and parseable.	"Return a JSON object with keys sentiment (one of positive, neutral, negative) and reason (one sentence)".
The learner checks the first structured result against its consumer before scaling up.	A format that looks right can still fail on the detail the consumer cares about, such as a date format or an empty field.	The learner pastes the first generated CSV into the spreadsheet import and fixes the column order in the prompt before running the other 200 rows.

Served by: Output the next step can use

Alignment

Framework	Code	Asks	Objectives here
AI Fluency 4D (Dakan and Feller)	Description	State the goal, context and wanted output clearly, and refine it	structures-a-prompt, iterates-on-output, asks-for-structure