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Introduction The Recipe Pattern is a way to deep dive on an high-level set of instructions to discover a define more specific steps. The pattern assumes that there is a clear intention, an high-level knowledge of the topic and the need to deep dive or validate the details. The pattern is mostly focused on “searching” or confirming the general ideas. The Prompt A well-known definition is the following: -I would like to achieve X -I know that I need to perform steps A,B,C Provide a complete sequence of steps for me Fill in any missing steps (Optional) Identify any unnecessary steps In this way the objective of the request is defined at the beginning, some high level instructions are given as a path and conditions are defined. ...

Introduction The “Flipped Interaction Pattern” is, essentially, a variant of the twenty questions game, played between the LLM and the user. The pattern flips the interaction, moving the LLM to ask to the users about a specific topic. To simplify any further answer coming from the user help the LLM to narrow down the number of possibilities, coming, at the end to a smaller set of solutions. Fun to use, fun to play. ...

Introduction The “Audience Persona Pattern” is probably the simplest and the most intuitive pattern in prompt engineering. It is a one-to-one mapping on a common way to say like “explain me like I’m 5”. It is probably the first pattern that anybody can adopt and, to be honest, probably the most useful. The Audience Persona Pattern is mostly useful in the situation when the user wants to target the answer to a specific audience, either a third party or themselves. ...

Introduction The “Question Refinement Pattern” is focused on improvements on the question itself, to help the LLM to get the “correct” answer. Require a quite high level of interaction with the LLM and, from the human, a check every time. Essentially the idea is asking to the LLM to fine tune the answer itself. The pattern makes the implicit assumption that the initial question is pretty broad or vague. The Prompt A way to apply the prompt is the following ...

Introduction Let’s go back to prompt engineering. One of the patterns that seems interesting to use is called Cognitive Verifier Pattern. This pattern aims to use the LLM / GPT to question the request of the users with tailoered questions to, essentially, break down the initial request and focus the answer. The pattern requires a quite high level of interaction between the user and the LLM. The pattern is useful when the user has a generic and quite vague question, to narrow down the options. ...

Introduction Playing with prompts I found - credits on reddit - this quite interesting prompt. Playing with it with ChatGPT behaves quite well on acting as a teacher. Having tried, and failed, to read the Surely You’re Prompting, Mr. Feynman! book, here there is my little petty revenge… The Prompt So we have: System Section - It is essentially sending the initial conditions Context section - Scope and deep dive Instruction - What the LLM has to do and follow Constrains - Quite self explanatory Output Format - How to answer, quite self explanatory too Success Criteria - This is more interesting, as filter the output User Input - The start <System> You are a brilliant teacher who embodies Richard Feynman's philosophy of simplifying complex concepts. Your role is to guide the user through an iterative learning process using analogies, real-world examples, and progressive refinement until they achieve deep, intuitive understanding. </System> <Context> The user is studying a topic and wants to apply the Feynman Technique to master it. This framework breaks topics into clear, teachable explanations, identifies knowledge gaps through active questioning, and refines understanding iteratively until the user can teach the concept with confidence and clarity. </Context> <Instructions> 1. Ask the user for their chosen topic of study and their current understanding level. 2. Generate a simple explanation of the topic as if explaining it to a 12-year-old, using concrete analogies and everyday examples. 3. Identify specific areas where the explanation lacks depth, precision, or clarity by highlighting potential confusion points. 4. Ask targeted questions to pinpoint the user's knowledge gaps and guide them to re-explain the concept in their own words, focusing on understanding rather than memorization. 5. Refine the explanation together through 2-3 iterative cycles, each time making it simpler, clearer, and more intuitive while ensuring accuracy. 6. Test understanding by asking the user to explain how they would teach this to someone else or apply it to a new scenario. 7. Create a final "teaching note" - a concise, memorable summary with key analogies that captures the essence of the concept. </Instructions> <Constraints> - Use analogies and real-world examples in every explanation - Avoid jargon completely in initial explanations; if technical terms become necessary, define them using simple comparisons - Each refinement cycle must be demonstrably clearer than the previous version - Focus on conceptual understanding over factual recall - Encourage self-discovery through guided questions rather than providing direct answers - Maintain an encouraging, curious tone that celebrates mistakes as learning opportunities - Limit technical vocabulary to what a bright middle-schooler could understand </Constraints> <Output Format> **Step 1: Initial Simple Explanation** (with analogy) **Step 2: Knowledge Gap Analysis** (specific confusion points identified) **Step 3: Guided Refinement Dialogue** (2-3 iterative cycles) **Step 4: Understanding Test** (application or teaching scenario) **Step 5: Final Teaching Note** (concise summary with key analogy) *Example Teaching Note Format: "Think of [concept] like [simple analogy]. The key insight is [main principle]. Remember: [memorable phrase or visual]."* </Output Format> <Success Criteria> The user successfully demonstrates mastery when they can: - Explain the concept using their own words and analogies - Answer "why" questions about the underlying principles - Apply the concept to new, unfamiliar scenarios - Identify and correct common misconceptions - Teach it clearly to an imaginary 12-year-old </Success Criteria> <User Input> Reply with: "I'm ready to guide you through the Feynman learning process! Please share: (1) What topic would you like to master? (2) What's your current understanding level (beginner/intermediate/advanced)? Let's turn complex ideas into crystal-clear insights together!" </User Input> The Output - ChatGPT The output from ChatGPT - 5.1 is quite well defined. ...

As everyone else I’m playing with GenAI too. It is fun, it is an hype, it is part of “OMG what is this stuff” moment. One of the various tutorial, on Claude, explains the concept of “few shots prompting. Long story short is giving an example to the AI to make it understading the “mood” and the context and how to answer to the question. So, according to the sample below, I know, horrible screenshot: ...

And here we are, just another blog. So vintage a blog in 2025. Why a blog? To be honest the idea has come on top of my mind just a couple of days ago. Just to check if was possible putting together a simple website on how to write stuff, publish doodles and try to write something in english that it is not only an email. After a couple of evenings, with the help of chatGPT here we are: ...