Anatomy of a Great Prompt
What you'll be able to do after this
- ✓Diagnose exactly why a weak prompt is weak — and fix it in under 2 minutes
- ✓Write any prompt using the 5-block RCTFE structure so the model knows its role, task, constraints, format, and examples
- ✓Apply a fill-in-the-blank master template to any new job without starting from scratch
- ✓Decide in 30 seconds whether to add examples (few-shot) or keep it zero-shot — and know why it matters
- ✓Route structured prompts to the right model: when Claude's precision wins, when Gemini's capacity wins
Why “Be More Specific” Is Bad Advice
Most AI advice sounds like this: “write better prompts,” “be more specific,” “give it context.” That advice is technically correct and completely useless. You can't improve something you haven't decomposed.
A prompt isn't one thing — it's five things that the model reads and processes in sequence. When the output is wrong, one of those five things is missing or broken. Once you know which slot is empty, the fix is obvious. Without the framework, you're guessing.
This lesson gives you the five building blocks, shows you what failure looks like without them, and hands you a reusable template you can drop into your Reusable Prompt Library today.
“The model doesn't know what you know. Every missing block is a gap it fills with a plausible guess — which is usually wrong in exactly the subtle way that wastes your time.”
The 5 Building Blocks: RCTFE
Every reliable prompt contains these five elements. You won't always use all five — but you should consciously decide which to include and which to skip, not just forget them.
The acronym is RCTFE. That's awkward to say aloud, so think of it as a checklist: Role · Context · Task · Format · Examples. Before you send a prompt, scan the list. Any missing block is a coin flip for what you get back.
Before / After: See the Difference
Here is the same job — summarize a meeting — written as a weak one-liner and then rebuilt block by block into a prompt that produces consistent, usable output.
What's missing: No role (which expert lens?), no context (who reads this? what do they care about?), no format (bullet list? table? prose?), no examples. The model will produce a generic 3-paragraph text that restates the meeting in order — rarely useful for anything.
The strong prompt produces a table your VP can scan in 90 seconds. The weak prompt produces a wall of text they'll skip. Same model, same transcript, completely different results — because the structure left nothing to chance.
The Master Template
Here is the RCTFE structure as a fill-in-the-blank template. Copy it into your Reusable Prompt Library as prompt-master-template.md. Whenever you need to build a new prompt from scratch, open this file instead of a blank page.
The {variables} in curly braces are yours to fill before each run. The comments after each block (what the audience cares about, what NOT to do) are reminders to yourself — delete them when you save a finished prompt. What stays is the structure.
When to Add Examples (and When Not To)
The E in RCTFE is optional — but it's the single highest-leverage block when the output standard is hard to describe in words. The decision rule is simple:
Zero-shot (no examples)
- ◦The output format is fully specifiable in words (e.g. a markdown table with named columns)
- ◦The task is a standard job the model has seen thousands of times (summarize, translate, classify)
- ◦You want speed — examples add tokens and latency
- ◦You're iterating quickly and don't have a worked example yet
Few-shot (1–3 examples)
- ✓The quality bar is hard to articulate but you know it when you see it (tone, voice, judgment calls)
- ✓The task involves classification or labeling with non-obvious category boundaries
- ✓Previous zero-shot runs were consistently off in the same specific way
- ✓You have a gold-standard example of good output you've already produced
The most useful example you can include is not just a “good” output — it's a good and a bad output side by side, with a one-line note explaining the difference. That contrast teaches the model the boundary you care about more precisely than any description.
Practical rule: Start zero-shot. If the output is consistently wrong in the same direction after two tries, add one example of what you want and one of what you got. That single comparison usually fixes it.
Routing: Claude vs Gemini for Structured Prompts
The RCTFE structure works on both models. But they respond differently to the five blocks — and knowing the differences makes your prompts more reliable on the first run.
| What you need | Use Claude | Use Gemini |
|---|---|---|
| Strict format compliance | Yes — Claude follows explicit format instructions more reliably; rarely invents extra sections | Sometimes adds unrequested sections or reformats; specify format twice if critical |
| Few-shot examples (1–3) | Yes — highly consistent at matching example patterns, even subtle ones | Good for structural patterns; less reliable at matching tone or voice from examples |
| Long input (50k+ words) | Limited — use Gemini for initial intake; hand structured output to Claude | Yes — long-context intake is Gemini's primary strength; process the whole doc in one pass |
| Judgment-heavy tasks (nuanced role / constraints) | Yes — the R and C blocks land better; Claude follows unstated implications of the role | Literal — give it explicit rules rather than expecting it to infer from context |
| High output volume (many rows, long tables) | Fine for moderate volume; may truncate very long structured outputs | Yes — handles high-volume structured generation without truncation |
The default routing rule for structured prompts: Claude for precision, Gemini for volume. If you need the output to look exactly like your template says, use Claude. If your input is massive and the structure is straightforward, use Gemini. For the most demanding work — large input, precise output — route through both: Gemini to process the raw material, Claude to shape the final result.
For a full routing table across all task types, see the Gemini vs Claude lesson.
Now build a library of these prompts
The next lesson walks you through setting up a versioned prompt library with 5 ready-to-use templates built on the RCTFE framework — your first compounding AI OS asset.