Claude prompts: examples for better answers

Claude is a model developed by Anthropic that stands out for handling long texts, reasoning about complex documents and producing text in a formal or professional tone. Using it well is not a matter of “magic” in the prompt, but of giving the right instructions in the right way.

A effective prompt for Claude always has three components: the context (who you are, what you're doing), the precise task (what it has to produce), and the format constraints (length, style, structure). If one of the three is missing, the output loses quality —not because Claude is "lazy", but because he works well with clear specifications, better than he does with ambiguity.

In this guide we see concrete examples, the most frequent errors and how to apply these principles in a business context. If you want to understand first how Claude works in general, read Claude AI: what it is and when to use it.

How a prompt works for Claude: the three key elements

Claude, like all linguistic models, does not "read the mind": he starts exactly from what you write. A well-structured prompt has three components that work together.

1. Context. Who you are, what role you're using Claude in, what material you're working on. Example: "I am an HR manager of a manufacturing SME. I have a 30-page contract to analyze." This orients Claude on the type of output that is useful to you.

2. Precise task. What Claude has to produce, with what goal. It does not "analyze this contract" but "identifies clauses that imply economic obligations for our company and reports those with indefinite duration."

3. Format Constraints. As you like the output: list, table, paragraph, JSON, maximum N words. If you don't specify it, Claude chooses for himself — and often chooses correctly, but not necessarily in the format that best integrates into your workflow.

To understand the theory behind the prompt structure, read the guide on prompt engineering: what it is and how to get started.

Generic Prompt vs Prompt Optimized for Claude

The difference between a prompt that produces mediocre output and one that produces useful output is often in specificity, not in length. Here is a direct comparison on real cases.

The rule: the more complex the task or the context is specific, the more the prompt needs to be detailed. Claude handles long instructions without problems — unlike models optimized for short exchanges.

Practical examples of prompts for Claude

Long documents and textual analysis

Claude handles very extensive contexts in a single conversation — one of his strengths compared to other models. Claude AI for long documents.

Example — contract analysis:

You are a legal consultant helping Italian SMEs. Read the following supply contract (provided after this message). Produce:
1. A table with the main clauses (clause name, concise content, risk to the client: high/medium/low)
2. A list of clauses with an indefinite term or automatic renewal
3. Three questions a lawyer should ask the supplier before signing

[contract text]

Example — technical document summary:

Read the following technical document (tender specifications, 45 pages). Produce an executive summary of max 400 words for a business owner without a technical background. Structure: the problem the contract solves → key requirements → evaluation criteria → important deadlines.

[document]

Professional Writing and Editorial Review

Claude produces texts in formal Italian of good quality. To deepen the use in writing, read Claude AI to write better.

Example — Tone Review:

You are a senior editor for Italian B2B business communications. Revise the following text:
- Keep the content unchanged
- Replace vague terms ("innovative solutions", "cutting-edge") with concrete descriptions
- Reduce the length by 20% by removing redundancies
- Produce the revised text + a list of the 3-5 main changes with reasons

[original text]

Example : build by structure:

Write the "Methodology" section of a sales proposal for a client in the logistics sector. The methodology consists of 3 phases: process audit (2 weeks), AI agent configuration (4 weeks), testing and handover (2 weeks). Tone: professional but not technical. Length: 250-300 words. Language: Italian.

Reasoning and Data Analysis

You have received 30 reviews of a B2B SaaS service (insert below).
Perform this analysis in sequence:
1. List recurring negative themes (at least 3 mentions to qualify as "recurring")
2. For each theme: how many mentions, representative quote, perceived impact (frustrant/blocker/minor)
3. Propose 1 concrete corrective action for each, formulated as a task for the product team
Final output: 4-column table (theme | Mentions | impact | corrective action)
[reviews]

Claude prompt in Italian: how to specify the register

Claude understands Italian without special instructions. The critical point is not the language, but the register: formal, technical, informative, commercial. If you do not specify it, Claude makes a reasonable choice but not necessarily the one you need.

Examples of specific useful registers:

• "Formal tone, suitable for an Italian B2B commercial letter"
• “Write for a non-technical entrepreneur aged 50; avoid unexplained acronyms.”
• "Disclosure log, blog article style, no legal jargon"
• "Technical-specialist, target: IT manager of Manufacturing SMEs"

The log counts mainly on emails, proposals, customer documents. For internal texts or analysis, Claude is already fine with the default.

How Claude differs from ChatGPT in the use of prompts

If you are already familiar with the prompt for ChatGPT, you will notice some practical differences. For a full comparison between the two models, read ChatGPT vs Claude: which one to choose.

Mistakes to Avoid

• Prompt too vague. "Improving this text" says nothing: improve for whom, in what direction, with what constraints? Always specify the improvement criterion.
• Not providing the material. "Analyze my contract" without pasting the contract forces Claude to invent. The text must be in the prompt, not described in words.
• Contradictory instructions. "Be brief but exhaustive" creates ambiguity. Choose: either "maximum 200 words" or "cover all critical points without limit". Never both.
• Not specifying the output. If you want a table, ask for a table. If you want JSON, show the format. Claude does not read the mind, but follows precise instructions with high fidelity.
• Ignoring the AI Hallucinations. Claude can produce citations, norms or invented data with authoritative tone. For factual content (laws, prices, dates), always verify the original source.

How to apply it in the company

Prompts for Claude become really useful when they come out of individual tests and enter processes. Here are three typical applications for SMEs.

Professional firm (lawyers, accountants). A prompt template for analyzing contracts or reviewing tax documents used by the whole team. Claude extracts the structured information, the professional verifies and decides. Result: less time on top-level reading, more focus on exceptions. Read more in Claude AI for companies.

Commercial office. A standard prompt to generate bid drafts from trade notes. The salesman inserts 5-10 bullet on the needs of the customer, Claude produces the draft in company format, the salesman corrects and sends. Estimate times halved.

Customer service. A prompt to classify incoming requests and suggest the type response. The operator already receives the categorization and a draft: approve, edit, send. No writing from scratch.

In all three cases the point is the same: Claude does not replace the person, he frees them from the first repetitive draft. If you want to understand how to turn these prompts into automated flows, our AI agents They do exactly that.

Conclusion

Writing effective prompts for Claude does not require special skills: it requires method. Context + Task + Format is the formula. The more complex the task, the more it is worth investing thirty seconds more to structure the prompt. the result changes substantially.

Claude excels where other models struggle: long documents, multi-step reasoning, formal texts in Italian. Exploiting these strengths means building prompts that activate them, not generic prompts that could go to any model.

If you want to go from testing with ChatGPT to an AI system integrated into business processes, Giallo Studio designs custom-made agents and automations. Or starts with driving prompt engineering: what it is and how to get started to deepen the theory.