{"id":36043,"date":"2026-05-19T09:05:54","date_gmt":"2026-05-19T07:05:54","guid":{"rendered":"https:\/\/www.azzurrodigitale.com\/?p=36043"},"modified":"2026-11-03T15:03:14","modified_gmt":"2026-11-03T14:03:14","slug":"prompt-engineering-the-art-of-talking-to-ai","status":"publish","type":"post","link":"https:\/\/www.azzurrodigitale.com\/en\/prompt-engineering-the-art-of-talking-to-ai\/","title":{"rendered":"Prompt Engineering: The Art of Talking to AI and Getting Real Results"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_89 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Indice dei contenuti<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #21bdff;color:#21bdff\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #21bdff;color:#21bdff\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.azzurrodigitale.com\/en\/prompt-engineering-the-art-of-talking-to-ai\/#Same_AI_opposite_results\" >Same AI, opposite results<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.azzurrodigitale.com\/en\/prompt-engineering-the-art-of-talking-to-ai\/#Easy_to_get_wrong\" >Easy to get wrong<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.azzurrodigitale.com\/en\/prompt-engineering-the-art-of-talking-to-ai\/#Deep_Tech_Technical_Insights_and_Best_Practices\" >Deep Tech: Technical Insights and Best Practices<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.azzurrodigitale.com\/en\/prompt-engineering-the-art-of-talking-to-ai\/#Conclusion_and_Next_Episode\" >Conclusion and Next Episode<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.azzurrodigitale.com\/en\/prompt-engineering-the-art-of-talking-to-ai\/#Frequently_Asked_Questions_FAQ\" >Frequently Asked Questions (FAQ)<\/a><\/li><\/ul><\/nav><\/div>\n\n<h4 class=\"wp-block-heading\"><strong>Written by Mattia Gottardello &#8211; Full-stack Developer in AzzurroDigitale<\/strong><\/h4>\n\n<p class=\"wp-block-paragraph\">In the previous episode \u2014 \u201c<em><strong><a href=\"https:\/\/www.azzurrodigitale.com\/en\/what-are-llms\/\">Beyond the Hype<\/a><\/strong><\/em>\u201d<em> \u2014 in the previous episode, \u201cBeyond the Hype\u201d \u2014 we dismantled the mystique surrounding LLMs: they\u2019re not oracles, but statistical engines that predict the next word. Good. Now that we know <strong>what an LLM is<\/strong>, we need to learn how <strong>to drive it<\/strong>. Because the difference between a mediocre answer and an output that saves you two hours of work isn\u2019t the model itself \u2014 it\u2019s how you ask.   <\/em><\/p>\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Same_AI_opposite_results\"><\/span>Same AI, opposite results<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n<p class=\"wp-block-paragraph\">Prompt Engineering is not black magic \u2014 it\u2019s <strong>process optimization<\/strong>. The same technology can produce radically different results depending on how the request is structured. <br\/><br\/>A poorly written prompt is like assigning a task to an intern on their first day with zero context: you end up with generic, off-tone text that has to be rewritten from scratch.<br\/><br\/>A well-structured prompt is like working with a senior colleague: they receive the instructions, understand the context, and deliver something usable on the first try.<br\/><br\/>Translated into concrete business terms:<\/p>\n\n<ul class=\"wp-block-list\">\n<li><strong>Sales emails<\/strong> that match your tone of voice instead of sounding robotic.<\/li>\n\n\n\n<li><strong>Reports and document summaries<\/strong> already formatted according to your internal standards, without manual rework.<\/li>\n\n\n\n<li><strong>Consistent outputs at scale<\/strong>, independent of daily mood swings or rushed execution.<\/li>\n<\/ul>\n\n<p class=\"wp-block-paragraph\">The ROI is easy to calculate: less time spent correcting, more time spent making decisions.<\/p>\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Easy_to_get_wrong\"><\/span>Easy to get wrong<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n<p class=\"wp-block-paragraph\">Let\u2019s take a common daily task: <em>writing an email to a customer about a delivery delay.<\/em><\/p>\n\n<p class=\"wp-block-paragraph\">Bad Prompt (lazy zero-shot)<\/p>\n\n<figure class=\"wp-block-image size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1006\" height=\"58\" src=\"https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-22-1.png\" alt=\"\" class=\"wp-image-36030\" srcset=\"https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-22-1.png 1006w, https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-22-1-300x17.png 300w, https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-22-1-768x44.png 768w\" sizes=\"(max-width: 1006px) 100vw, 1006px\" \/><\/figure>\n\n<p class=\"wp-block-paragraph\"><br\/>Result: an anonymous, overly formal text written in stereotypical corporate English, signed \u201cThe Team.\u201d Completely unusable. <br\/><br\/><strong>Proper Prompt<\/strong> (structured)<\/p>\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1004\" height=\"225\" src=\"https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-11.png\" alt=\"\" class=\"wp-image-36031\" srcset=\"https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-11.png 1004w, https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-11-300x67.png 300w, https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-11-768x172.png 768w\" sizes=\"(max-width: 1004px) 100vw, 1004px\" \/><\/figure>\n\n<p class=\"wp-block-paragraph\">Same model, completely different outcome.<br\/>The rule is simple: <strong>give the AI the same context you would give a human being<\/strong>. Role, objective, constraints, output format. If omitting a detail would confuse a colleague, it will confuse the model too.   <\/p>\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h2 class=\"wp-block-heading has-text-color has-link-color has-large-font-size wp-elements-1\" style=\"color:#01bdff\"><span class=\"ez-toc-section\" id=\"Deep_Tech_Technical_Insights_and_Best_Practices\"><\/span>Deep Tech: Technical Insights and Best Practices<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n<p class=\"wp-block-paragraph\">When moving from chat interfaces to <strong>API <\/strong>integrations, the game changes. The first key distinction is architectural: <\/p>\n\n<ul class=\"wp-block-list\">\n<li><strong>System Prompt<\/strong> \u2014 The persistent \u201crules of engagement\u201d for the agent: who it is, what it can and cannot do, and the required response format. Defined once. <\/li>\n\n\n\n<li><strong>User Prompt<\/strong> \u2014 The specific request from the end user, changing with every call.<\/li>\n<\/ul>\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1007\" height=\"228\" src=\"https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-00-1.png\" alt=\"\" class=\"wp-image-36032\" srcset=\"https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-00-1.png 1007w, https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-00-1-300x68.png 300w, https:\/\/www.azzurrodigitale.com\/wp-content\/uploads\/2026\/05\/image_2026-05-15_18-06-00-1-768x174.png 768w\" sizes=\"(max-width: 1007px) 100vw, 1007px\" \/><\/figure>\n\n<p class=\"wp-block-paragraph\">Two techniques I use daily in producion:<\/p>\n\n<p class=\"wp-block-paragraph\"><strong>Few-Shot Prompting<\/strong> \u2014 Include 2\u20133 input\/output examples directly in the payload. The model learns the pattern far better than through abstract descriptions alone. This is essential for custom taxonomies, structured data extraction, domain-specific classifications.  <\/p>\n\n<p class=\"wp-block-paragraph\"><br\/><strong>Chain-of-Thought<\/strong> \u2014 you force the model to reason before answering. Typical instruction: &#8220;Analyse the steps in a reasoning field, then return the final result in output&#8221;. <a href=\"https:\/\/www.azzurrodigitale.com\/en\/llm-and-rag\/\">Drastically reduces hallucinations<\/a> on logical tasks, at the cost of a few extra tokens. When the output has to be JSON consumed by other code, this pattern makes the difference between a reliable integration and a production bug at 3 a.m.  <\/p>\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion_and_Next_Episode\"><\/span><strong>Conclusion and Next Episode<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n<p class=\"wp-block-paragraph\">Today, the best code is no longer made only of Python, TypeScript, or Go: it also includes <strong>well-engineered natural language instructions<\/strong>. Treat prompts the same way you treat code: version them, test them, document them. <\/p>\n\n<p class=\"wp-block-paragraph\"><br\/><strong>Next episode \u2014<em>&#8220;<a href=\"https:\/\/www.azzurrodigitale.com\/en\/llms-in-production-security-costs-and-integration-challenges\/\">LLMs in Production: Costs, Security and Integration<\/a>&#8220;<\/em><\/strong>: how to bring these models onto company servers without blowing the budget or risking data leaks. We&#8217;ll talk about token economics, RAG, self-hosting and privacy. Stay tuned.  <\/p>\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions_FAQ\"><\/span><strong>Frequently Asked Questions (FAQ)<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n<p class=\"wp-block-paragraph\"><strong>What is prompt engineering?<\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">It is the practice of structuring requests to an artificial intelligence model to get useful, consistent outputs. It is not magic but process optimisation: the quality of the answer depends on how the request is set up. <\/p>\n\n<p class=\"wp-block-paragraph\"><strong>How do you write a good prompt?<\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">By giving the AI the same context you would give a colleague: role, goal, constraints and output format. If an omitted detail would confuse a person, it will confuse the model too. <\/p>\n\n<p class=\"wp-block-paragraph\"><strong>What is the difference between a system prompt and a user prompt?<\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">The system prompt defines the assistant&#8217;s persistent rules: who it is, what it can do and in what format it must answer. The user prompt is the user&#8217;s individual request, which changes with every call. <\/p>\n\n<p class=\"wp-block-paragraph\"><strong>What is few-shot prompting?<\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">It is a technique in which you include 2-3 input and output examples in the prompt: the model learns the pattern better than from an abstract description. It is useful for classifications, taxonomies and structured data extraction. <\/p>\n\n<p class=\"wp-block-paragraph\"><strong>What is chain-of-thought for?<\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">It asks the model to spell out its reasoning before the final answer. It reduces hallucinations in logical tasks, at the cost of a few extra tokens, and is useful when the output has to be used by other code. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>The difference between a trivial answer and an output that saves you two hours of work does not lie in the AI model. It lies in how you ask. Here&#8217;s how to do it well.  <\/p>\n","protected":false},"author":2,"featured_media":37340,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[388],"tags":[],"class_list":["post-36043","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-adtech"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Prompt Engineering: How to Talk to AI and Get Real Results - AzzurroDigitale<\/title>\n<meta name=\"description\" content=\"Prompt engineering in practice: how to structure effective AI requests with few-shot and chain-of-thought techniques and get real results every time.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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