1. Being Clear and Direct
Claude doesn't have access to what's in your head — only to what's in the prompt. A vague instruction leaves it guessing at exactly the things you care about most: scope, format, tone, and what "done" looks like. The fix isn't a magic phrase, it's just... saying the quiet part out loud.
VAGUE:
"Write something about our new pricing page."
DIRECT:
"Write a 150-word announcement for our new pricing page, for existing
customers via email. Tone: friendly, not salesy. Lead with the fact that
existing plans aren't changing. End with a single clear CTA: 'See the new
plans' linking to /pricing. Do not mention specific dollar amounts."
The direct version answers, up front, every question Claude would otherwise have to guess at: length, audience, tone, structure, and an explicit constraint (no dollar amounts) that a vague prompt would never have surfaced until you saw the wrong output and had to correct it. Every one of those specifics you supply is one less thing Claude has to guess — and less guessing means less rework for you.
Read your prompt back and ask: "if a new hire with no context read only this, would they produce what I actually want?" If the honest answer is "they'd have to guess at three things," those three things belong in the prompt.
2. Give Claude Context, Not Just Instructions
An instruction says what to do. Context says why, and why matters more than it seems — it lets Claude make good judgment calls on the countless small decisions an instruction alone doesn't cover.
INSTRUCTION ALONE:
"Summarize this contract in 3 bullet points."
INSTRUCTION + CONTEXT:
"Summarize this contract in 3 bullet points. This is for a non-lawyer
founder deciding whether to sign — focus on financial obligations,
termination conditions, and anything unusual compared to a standard SaaS
agreement. Skip standard boilerplate clauses entirely."
With just the instruction, Claude has to guess what "important" means for a 3-bullet summary of a legal document — and a reasonable guess might pick the wrong three things for your actual purpose. The context version tells Claude who's reading it and what decision it's for, which changes what counts as important information in a way no amount of rephrasing the instruction alone could fix.
"Who is this for, and what will they do with the answer?" is often the single highest-leverage sentence you can add to a prompt — it silently answers dozens of smaller formatting and content decisions at once.
3. Few-Shot Examples
Sometimes the shape of a good answer is much easier to show than to describe. Few-shot prompting means giving Claude one or more examples of input paired with the exact kind of output you want, before asking it to do the real one.
ZERO-SHOT:
"Classify this support ticket's urgency: 'My export button is grayed out
on Tuesdays only, otherwise fine.'"
→ Claude has to guess your urgency scale AND your output format.
FEW-SHOT:
"Classify ticket urgency as LOW, MEDIUM or HIGH.
Ticket: 'Site is completely down, we're losing sales right now.'
Urgency: HIGH
Ticket: 'Small typo in the footer copyright year.'
Urgency: LOW
Ticket: 'My export button is grayed out on Tuesdays only, otherwise fine.'
Urgency:"
Two examples pin down both the exact label vocabulary (LOW/MEDIUM/HIGH, not "urgent"/"not urgent") and the exact output format (just the label, no explanation) — both things that would otherwise require a paragraph of instructions to specify, and even then might not be followed as reliably as two clean examples achieve immediately.
Two or three examples that only cover the "obvious" cases won't help with the ambiguous ones. Include at least one example near a boundary (a ticket that's borderline MEDIUM/HIGH, say) so Claude sees how you actually want ties broken.
5. Hands-on Exercise
Rewrite three vague prompts, then prove few-shot changes the output
Turn today's four techniques into muscle memory by applying them to your own real prompts, not toy examples.
Part 1 — Fix your own prompts:
- Find (or write) three prompts you'd realistically send Claude for your own work — one for writing, one for analysis, one for code or data.
- For each, rewrite it to be clear and direct (explicit format, length, audience, constraints) and add relevant context (who it's for, what happens with the output).
- Run the before and after versions and compare. Note the single biggest difference in the output for each pair.
If you're struggling to find a real prompt, use one from a task you did manually this week — an email you wrote, a summary you had to produce, a small script you wrote by hand.
Part 2 — Prove few-shot changes behavior:
Pick a classification or formatting task (sentiment, urgency, extracting a field, converting to a specific format).
- Run it zero-shot (instructions only) on 3-5 inputs and note the output format Claude chose on its own.
- Add 2-3 examples showing your exact desired label set and format, including one edge case.
- Re-run the same 3-5 inputs and compare format consistency and edge-case handling before and after.
6. Knowledge Check
Four quick questions. Expand each to check your answer.
Q1
What's the practical test for whether a prompt is "clear and direct enough"?
What's the practical test for whether a prompt is "clear and direct enough"?
Whether someone with no context, reading only the prompt, would produce what you actually want. Anywhere they'd have to guess — format, length, tone, audience, constraints — is a gap Claude has to guess at too, and belongs explicitly in the prompt.
Q2
What does adding context (beyond the instruction itself) actually change about Claude's output?
What does adding context (beyond the instruction itself) actually change about Claude's output?
It lets Claude make better judgment calls on everything the instruction doesn't explicitly cover — like which details in a document count as "important" for a given reader and purpose. The same instruction with different context can correctly produce very different outputs.
Q3
Why do good few-shot examples need to include edge cases, not just obvious ones?
Why do good few-shot examples need to include edge cases, not just obvious ones?
Obvious examples only show Claude how to handle cases it would likely have gotten right anyway. The value of few-shot is showing how ambiguous or borderline cases should be resolved — that's exactly where a model left to guess is most likely to diverge from what you actually want.
Q4
When is it worth wrapping parts of a prompt in XML tags instead of writing plain paragraphs?
When is it worth wrapping parts of a prompt in XML tags instead of writing plain paragraphs?
Once a prompt has multiple distinct parts — reference material, examples, instructions, formatting rules — that plain prose could blur together. Tags remove ambiguity about which text is content to act on versus an instruction to follow, which matters most on long or multi-part prompts.