How the Builders Prompt

Module 10 taught you the foundation of prompting: be specific, give context, set constraints, describe what “done” looks like, iterate one change at a time. If you have been prompting daily, you already do most of that without thinking.

This module is the next layer — a small set of techniques that the people who built Claude reach for when a plain instruction is not getting the result. They are not tricks. Each one changes how the model processes your request, and each one earns its place by fixing a specific failure mode: vague reasoning, wrong format, confident-but-wrong answers, lost details in a long document.

What you already have vs. what is new

Be honest about the gap. The workshop covers about a dozen techniques. Roughly half of them you already learned in Modules 1 and 10:

You do not need to relearn the left column. This module is the right column.

Self-assessment: where is your gap?

Work through this list honestly. For each item you cannot do confidently today, there is a lesson that teaches it.

1. Prime the model’s behavior before you give it the task.

Opening a prompt with “You are a senior security reviewer auditing this code for a government deployment” changes the depth, vocabulary, and caution of everything that follows. If you only ever describe the task and never the role, you are leaving the easiest win on the table.

→ Give the Model a Role

2. Show the model what “good” looks like instead of describing it.

For anything with a consistent shape — a report format, a commit message style, a classification — two or three examples teach the pattern better than a paragraph of instructions ever will. Most people under-use examples or write inconsistent ones.

→ Show, Don’t Tell

3. Make the model reason before it commits to an answer.

On anything with logic — math, analysis, multi-step decisions — telling the model to work through its reasoning before giving the answer measurably improves accuracy. This is a prompt technique, distinct from the “thinking effort” setting your tool may expose.

→ Let the Model Think

4. Separate your instructions from your data so the model never confuses the two.

When you paste a long document plus a question, the model can blur which part is the task and which part is the input. Wrapping each part in clearly labeled tags fixes that — and lets you control the exact shape of the output.

→ Structure and Format

5. Get answers grounded in the source instead of plausible-sounding inventions.

The single highest-value technique for research, legal, compliance, and analysis work: make the model pull the relevant quotes from the source first, then answer using only those quotes. It is how you turn “sounds right” into “is right.”

→ Ground the Answer

6. Break a hard prompt into a chain where each step does one job.

When one prompt is trying to extract, analyze, and write all at once, quality drops. Chaining — feeding the output of one focused prompt into the next — beats a single overloaded prompt on complex work.

→ Chain Your Prompts

7. Know a prompt is good because you tested it — not because the first run looked fine.

A prompt that worked once can fail on the next input. Defining what a good answer looks like and running the prompt against a handful of cases is the difference between a prompt you hope works and one you know works.

→ Test and Keep What Works

How these stack

The techniques are independent — take them in any order based on your self-assessment. But they compound. A mature prompt for a hard task often uses several at once:

[ROLE]      You are a senior grants analyst.
[EXAMPLES]  Here are two examples of the summary format I want…
[DATA]      <proposal> …the full text… </proposal>
[THINK]     First, in <scratchpad>, note the budget, aims, and risks.
[GROUND]    Quote the exact budget lines before you total them.
[FORMAT]    Then output the summary as the example format above.

That is one prompt using role, examples, structure, thinking, and grounding together. You do not start there — you start with one technique that fixes your current problem, and you add the others as the task demands.

Key takeaways

• These are sourced techniques, not folklore. They come from Anthropic’s prompt engineering workshop and docs — the people who built the model, telling you how they intended it to be used.
• Half of prompting you already know. Be specific, give context, set constraints, describe the outcome. This module is the other half.
• Seven techniques fill the real gaps: role, examples, thinking, structure, grounding, chaining, and testing.
• Take them in any order. Use the self-assessment to find your gap and start there.
• The biggest single win for serious work is grounding — making the model quote its source before it answers. If you read one lesson, read that one.

Next: Give the Model a Role