# The Anatomy of a Good Prompt — Prompt Engineering

Source: https://www.geekswithgeeks.com/en/prompt-engineering/f-anatomy

> Break a prompt into role, task, audience, rules, format and input.

## Six building blocks

Reliable prompts usually contain: (1) a **role** or perspective ("support analyst"); (2) the **task** in one clear sentence; (3) the **audience** and purpose; (4) **rules and constraints** (length, tone, what to avoid, what to do when unsure); (5) the **output format**; and (6) the **input** itself, clearly separated from the instructions. Not every prompt needs all six, but checking this list quickly finds what is missing when output disappoints. Using a template also makes prompts consistent across a team.

## A reusable template, run

I ran this plain-Python (standard library only) example. The template fills in role, task, audience, rules, format and input, and prints the final prompt (259 characters, about 65 tokens by the rough 4-characters-per-token rule).

```python
from string import Template

PROMPT = Template("""You are a $role.
Task: $task
Audience: $audience
Rules:
$rules
Output format: $fmt

Input:
$text""")

rules = ["Use only the input text.", "Max 2 sentences.", "If unsure, say 'unknown'."]
p = PROMPT.substitute(
    role="support analyst",
    task="summarise the customer's complaint",
    audience="a busy manager",
    rules="\n".join(f"- {r}" for r in rules),
    fmt="plain text",
    text="My order 4821 arrived late and the box was damaged.",
)
print(p)
print("characters:", len(p), "| rough tokens:", round(len(p) / 4))

```

Output:

```
You are a support analyst.
Task: summarise the customer's complaint
Audience: a busy manager
Rules:
- Use only the input text.
- Max 2 sentences.
- If unsure, say 'unknown'.
Output format: plain text

Input:
My order 4821 arrived late and the box was damaged.
characters: 259 | rough tokens: 65
```

**Quiz:** Why separate the input from the instructions?

- [ ] Because models cannot read inputs
- [ ] To hide the prompt
- [ ] To save a token
- [x] So the model can tell what to do from what to process

*Answer:* So the model can tell what to do from what to process. Clear separation avoids confusion and reduces injection risk.