Why your team gets mediocre answers from AI

Almost every company already pays for ChatGPT, Claude or Gemini. And almost all of them have the same complaint: the answers are generic, they need manual fixing, and in the end "it's faster if I just do it myself." The problem is rarely the model. The problem is the prompt.

A prompt is a work instruction. If you give a new hire a one-line instruction with no context, no examples and no idea of what the finished result should look like, you'll get mediocre work. AI works exactly the same way. Prompt engineering is simply the discipline of writing those instructions so the output is useful on the first try.

In this guide you'll learn the structure we use at AvilaDev to write professional prompts, 5 techniques that work with any model, and how to turn good prompts into reusable templates for your whole team.

The anatomy of a professional prompt

After hundreds of AI projects, the structure that works best has five parts. Not all of them are required, but the more you include, the more predictable the answer will be.

Part	What it includes	Example
Role	Who the AI should "be"	You are a B2B copywriter who specializes in software
Context	Business details, audience, situation	Our client is a dental clinic in Madrid with 3 locations
Task	What it should do, with a clear verb	Write 3 email subject lines to win back inactive patients
Constraints	Limits on tone and length, and what it should NOT do	45 characters max, no emojis, no discounts
Format	How you want to receive the answer	Table with columns: subject line, angle, why it works

Compare these two prompts:

Weak prompt: "Write an email for inactive customers."
Professional prompt: "You are an email marketing specialist for dental clinics. Our clinic has patients who haven't come in for more than 12 months. Write a win-back email of no more than 120 words, in a warm but professional tone, inviting them to book a checkup. Don't offer discounts. Return the subject line, preheader and body separately."

The second one takes 40 more seconds to write and saves 15 minutes of edits.

5 prompt engineering techniques that work with any model

1. Few-shot: teach with examples

Instead of describing the style you want, show it. Paste 2 or 3 examples of good output (product descriptions, support replies, posts) and ask for a new one "in the same style." It's the technique with the best effort-to-results ratio, especially for keeping a consistent brand voice.

2. Ask it to think before answering

For analysis tasks (comparing vendors, reviewing a contract, prioritizing work), add: "Before giving your final answer, work through the problem step by step." This is known as chain of thought, and it noticeably reduces reasoning errors on multi-step problems.

3. Separate instructions from data with delimiters

When you paste in a document, an email or a transcript, wrap it in clear tags like <document>...</document>. That way the model doesn't confuse the content it should analyze with the instructions it should follow. It's also a basic safety measure against text that tries to "sneak in" instructions.

4. Break big tasks into chains

A single prompt that has to "research, write, review and translate" produces poor results. A prompt chain works better: first an outline, then a draft section by section, then a review against specific criteria. Every step can be checked, and you can fix things before moving on.

5. Define the exact output format

If the result is going into a spreadsheet, a CRM or an automation, ask for JSON or a table with defined columns. If a client is going to read it, specify length and structure. A defined format is what turns an "interesting" answer into a usable one.

💡 Rule of thumb

If you have to fix the same thing twice, it's not an AI problem: it's an instruction missing from your prompt. Add it to the template and you'll never have to fix it again.

From one-off prompts to a team template library

This is where companies see the real payoff. A great prompt that lives in one person's chat history doesn't scale. A shared prompt library turns what your best people know into a standard for everyone.

Identify the 10 repetitive tasks where your team already uses AI (emails, proposals, meeting summaries, support replies, posts).
Write one template per task using the 5-part anatomy, with variables in brackets: [client], [product], [tone].
Store them in one place: Notion, Google Docs, or each tool's custom "Projects" and "GPTs."
Assign an owner who reviews and improves the templates every month based on team feedback.
Measure the time saved: time per task before and after. That's the number that justifies continued investment.

Common mistakes to avoid

Pasting sensitive data without a policy: define which customer information can and can't go into AI tools.
Trusting without verifying: AI can make up data, figures and quotes with total confidence. Every factual claim gets checked.
Endless, contradictory prompts: longer isn't better. Clear and well organized is better.
Not iterating: your first prompt is a draft. Tweak it, test it and save the version that works.

Learn to apply it step by step with the course

This article is the starting point. If you want to put it into practice with examples, templates and videos that show each topic in action, check out the course Prompt Engineering: Professional Course for Teams.

Online course

Prompt Engineering: Professional Course for Teams

🎓 8 step-by-step modules, from the basics to implementation
🎬 6 hand-picked video tutorials to see each topic in action
📘 A 21-page downloadable workbook with templates and checklists
♾️ One-time payment, lifetime access and annual updates

Some of the modules:

Fundamentals of Effective Prompting
Advanced Prompting Techniques
Chain of Thought and Reasoning
Few-Shot, Zero-Shot, and Fine-Tuning

See the full course →

And if you'd rather have us build AI directly into your processes (chatbots, automations, agents), AvilaDev can do it for you. Tell us about your project.

#how to write prompts

Prompt Engineering for Teams: Techniques and Templates That Work (2026)

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