Writing Clear Instructions for Projects

𝗢𝗽𝗲𝗻𝗔𝗜 𝗷𝘂𝘀𝘁 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝗼𝗳𝗳𝗶𝗰𝗶𝗮𝗹 𝗚𝗣𝗧-4.1 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗴𝘂𝗶𝗱𝗲! It provides a detailed guide on how to steer GPT-4.1 with precision, including examples, tips, and advanced techniques. You can access the full version for free below. ⬇️ 𝗜𝗻 𝘀𝘂𝗺𝗺𝗮𝗿𝘆, 𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝗲𝗹𝗲𝗺𝗲𝗻𝘁𝘀: ➜ Be Clear with Your Instructions: GPT-4.1 is really good at following directions, but only if you're specific. The more clear and direct your prompt, the better the response. ➜ Break Down Complex Tasks: If you're working on something complicated, ask GPT-4.1 to “think step by step.” It helps the model give more accurate and thoughtful answers. ➜ Use Structure: If you need to share a lot of info, use clear structure—like markdown or bullet points. This helps GPT-4.1 understand and organize the info better. ➜ Format Your Prompts with Clear Sections: Structure your prompts for easier comprehension:   - Role and Objective   - Instructions (with subcategories)   - Reasoning Steps   - Output Format   - Examples   - Final instructions ➜ Put Important Instructions at the Start and End: For longer prompts, put your key instructions both at the beginning and the end. This helps the model stay on track. ➜ Guide It with Reminders: If you're designing a workflow or solving a problem, include reminders like “keep going until it’s fully resolved” or “plan carefully before acting.” This keeps the model focused. ➜ Use the Token Window Wisely: GPT-4.1 can handle a huge amount of text, but too much at once can slow it down. Be strategic about how much context you provide. ➜ Balance Internal and External Knowledge: For factual questions, tell GPT-4.1 to either “only use the provided context” or to mix that context with general knowledge. This helps you get the most accurate results. 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁: 𝗧𝗵𝗲 𝗸𝗲𝘆 𝘁𝗼 𝘂𝘀𝗶𝗻𝗴 𝗚𝗣𝗧-4.1 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝘁𝗵𝗮𝘁 𝗴𝘂𝗶𝗱𝗲 𝗶𝘁 𝘁𝗼𝘄𝗮𝗿𝗱 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗮𝗻𝘀𝘄𝗲𝗿. 𝗜𝘁’𝘀 𝗮𝗹𝗹 𝗮𝗯𝗼𝘂𝘁 𝗮𝘀𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆! Access it here or download it below: https://lnkd.in/dCm6DeFW

One of the good coding practices that I picked up while working at Google is about writing helpful error messages. Instead of simply stating the error, almost always, the error message also contains steps to resolve it, links to refer to, a quick shortcut to post questions to the internal question-answering platform, or at least the direction to look into. This practice can be seen across all Google Products. Try integrating with YouTube APIs, and you will see errors that are not only verbose but helpful. This can also be seen for all GCP interactions, be it web console or CLI, where the error message contains a link to a document on how to fix the issue. I have personally started using this practice every time I write code. #AsliEngineering

"Vibe Coding !== Low Quality Work: a guide to responsible AI-assisted dev" ✍️ My latest free article: https://lnkd.in/gjMdjMWV The allure of "vibe coding" – using AI to "move faster and break even more things" – is strong. AI-assisted development is undeniably transformative, lowering barriers and boosting productivity. But speed without quality is a dangerous trap. Relying uncritically on AI-generated code can lead to brittle "house of cards" systems, amplify tech debt exponentially, and introduce subtle security flaws. Volume ≠ Quality. A helpful mental model I discuss (excellently illustrated by Forrest Brazeal) is treating AI like a "very eager junior developer." It needs guidance, review, and refinement from experienced hands. You wouldn't let a junior ship unreviewed code, right? So how do we harness AI's power responsibly? I've outlined a field guide with practical rules: ✅ Always review: Treat AI output like a PR from a new hire. ✅ Refactor & test: Inject engineering wisdom – clean up, handle edge cases, test thoroughly. ✅ Maintain standards: Ensure AI code meets your team's style, architecture, and quality bar. ✅ Human-led design: Use AI for implementation grunt work, not fundamental architecture decisions. The goal isn't to reject vibe coding, but to integrate it with discipline. Let's use AI to augment our craft, pairing machine speed with human judgment. #softwareengineering #programming #ai

Stuck in an endless loop of client changes? Lost track of what revision this constitutes? Yeah. Been there. Done that. The secret? It's not about saying no. It's about saying yes to the right things upfront. Every project that goes sideways starts the same way: Vague agreements. Fuzzy boundaries. Good intentions. Six weeks later you're bleeding money and everyone's frustrated. Here's my framework after 30 years of running two 8-figure businesses: The SOW is your salvation. Not some boilerplate template. A real document that covers: • Exact deliverables (not "design work" but "3 homepage concepts, 2 rounds of revisions") • Hours of operation ("We respond M-F, 9-5 PST. Weekend requests get Monday responses") • Revision rounds spelled out ("Round 1 includes up to 5 changes. Round 2 includes 3.") • Feedback cycles defined ("48-hour turnaround for client feedback or the project may be delayed or additional fees may be incurred") But here's what most people miss— Don't work on client notes immediately. Client sends 37 pieces of feedback at 11pm Friday? Producer sends conflicting notes from the CEO? Marketing wants one thing, sales wants another? Stop. Collect everything first. Resolve the conflicts. Get on the phone and discuss it with your client to get alignment. Separate the "have to haves" from the "nice to haves". Then present unified changes. "Based on all feedback received, here are the 8 changes we'll implement. This constitutes revision round 2 of 3." Watch how fast the random requests stop. No extra work that goes unappreciated. No more feelings of being taken advantage of. Communicate before the crisis, prevents the crisis from happening. "Just so you know, we're entering round 2. You have one more included. After that, it's $X per additional round." No surprises. No awkward money conversations. No resentment. Scope creep isn't a them problem. It's a you problem. And that's good news, because that means you are in control. They're not trying to take advantage. They just don't know where the boundaries are because you never drew them. Draw the lines early. Communicate them clearly. Everyone wins. What's your most painful scope creep story? What boundary would've prevented it? Small Business Builders #projectmanagement #clientmanagement #businessgrowth

Anthropic’s “Prompting 101” is one of the best real world tutorials I’ve seen lately on how to actually build a great prompt. Not a toy example. They showcase a real task: analyzing handwritten Swedish car accident forms. Here’s the breakdown: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗶𝘀 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲. You don’t write the perfect prompt on the first try. You test, observe, refine. Just like any other product loop. 𝟮. 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. The best prompts follow a playbook: - Start with task + tone context - Load static knowledge into the system prompt - Give clear rules and step-by-step instructions - Show concrete examples - Ask the model to think step-by-step - Define structured output 𝟯. 𝗗𝗼𝗻’𝘁 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹, 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗱𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗶𝘁. In the first version, Claude hallucinated a skiing accident. Only after adding context, rules, and constraints did it produce reliable results. You wouldn’t let a junior analyst guess on regulatory filings. Don’t let your LLM do it either. 𝟰. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗺𝗽𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲. In traditional software, interfaces are buttons and APIs. In GenAI, the interface is language. Your prompt is the program. Most teams still treat prompts like notes in a playground. High-performing teams treat them like production code. That's why in our IBM watsonx platform, prompts are assets just like code or data. 👉Access the video tutorial here: https://lnkd.in/gUdHc2uy ________________________ ♻️ Repost to share these insights! ➕ Follow Armand Ruiz for more

15 years ago, I wrote "Write Better, Sell Better". The tagline that defined my career. Since then, I've written copy for Fortune 500 companies, projects that made millions, and taught copywriting at the university → Yep, writing is my life. But you don't have all semester long to learn. This post contains my 7 biggest writing lessons: (Save and repost this 60-second class ♻️) --- 1. Clarity is 90% of your writing Clarity beats cleverness 10 times out of 10. Remember: First, write clearly. Then, write creatively. Most focus on "creative" right off the bat = big no. (If you can do both, jackpot) --- 2. Use signposting to showcase authority I teach this technique to every single client. (This sentence you just read was a signpost) Signposts are short "signals" to your expertise and results. Think of them as signs on the side of the road, which allow you to "trust" the journey you're on. Can you notice where else I used it in this post? 😉 --- 3. Too much copy? Make it choppy. Here are 2 examples (the second one is better): Sometimes, we write super long sentences that we have no time to breathe and neither do our readers. Sometimes, we write super long sentences. We have no time to breathe. And neither do our readers. Chop it up! It's easier to read. --- 4. Write unselfishly. Edit relentlessly. Think about it. Writing is 80% editing. You are tweaking, optimizing, and making things sound perfect. One bit of advice: Don't hold back. Edit (ehm, delete) anything that isn't on-topic. I've deleted probably 40% of this post. --- 5. Use sensory words. Add texture to text. That had to hurt! → That had to "sting"! Having a bad day? → Having a "rough" day? A seamless process → A "silky-smooth" process All of the 2nd descriptions above can be "felt". It's how your readers "touch an emotion", not just "read text". --- 6. Never write to "everyone" Remember this for all time: "Everyone" is not your audience. Sales happen when your prospect can clearly "see" themselves in your writing. So how do you go about this? --- 7. The "Dear son" writing method Pick "one" person to speak to → I speak to my son. For you, it could be your best friend, the old you etc. I start everything with "Dear son" and end with "Love, Dad". In the end, I simply delete these 2 parts. Doing so creates such a powerful connection with your readers + a consistent tone of voice. With every word. Where there's connection, there's trust. And where there's trust, there's sales. Words to live by. --- Write Better, Sell Better = that's the whole game! Let me know which lesson you enjoy the most. Professor Jay, out. (Repost this for your network ♻️)

One of the easiest ways to improve your collaboration with product managers and engineers is to improve your design handoff files. - It doesn’t take a lot of time - It drastically improves how people interpret your designs - It reduces backs and forths between engineers and designers - It gives engineers confidence that they are doing the right thing - It increases the chances that what’s implemented will match the design Great handoff files, for me, are an instant sign of design maturity. It shows me that designers think not only of themselves but also the ecosystem of people around them. Coming up with great handoffs boils down to the following: - Setting context - Adding structure - Adding annotations - Including all states - Visualizing the flow - Including a prototype - Doing a run-through Not everything is required for every design initiative; a small feature update may not need a full handoff, whereas a big, impactful one may require everything from the above. In this cheat sheet, I’ve put together my top tips for delivering the “perfect” handoff file. Bonus: I have also created a Figma Annotation and handoff kit with handy components for the above. Find the link in the comments. 👇 — If you found this useful, consider reposting ♻️ PS: By handoff I'm not referring to the process of handing over a design, which should be a collaborative process from problem to solution, but rather the organized design file that is the single source of truth for what needs to be implemented. #uxdesign #uiux #productdesign

STOP asking ChatGPT to "make it better". Here's how to better prompt it instead: ☑ Clearly Identify the Issue Rather than a vague “make it better,” specify the exact element that needs change. For example: "Rewrite the second paragraph so it includes three concrete examples of our product’s benefits. The tone must be formal and persuasive. Remove any informal language or redundant phrases." ☑ Divide the Task into Discrete Steps Break the overall revision into a sequence of manageable tasks. For example: "Go through my instructions, step by step. – Step 1: Summarize it in one sentence. – Step 2: Identify two specific weaknesses. – Step 3: Rewrite the text to address these weaknesses, incorporating specific data or examples." ☑ Specify the Format and Level of Detail Define exactly how the final output should look. For example: "Provide the final revised text as a numbered list where each item contains 2–3 sentences. Each item must include at least one statistical fact or concrete example, and the overall response should not exceed 250 words." ☑ Request a Chain-of-Thought Explanation Ask the model to detail its reasoning process before giving the final output. For example: "Before providing the final revised text, explain your reasoning step-by-step. Identify which parts need improvement and how your changes will enhance clarity and professionalism. Then, present the final revised version." ☑ Conditional Instructions to Enforce Compliance Add if/then conditions to ensure all requirements are met. For example: "If the revised text does not include at least two concrete examples, then add a sentence with a real-world statistic. Otherwise, finalize the response as is." ☑ Consolidate All Instructions into One Prompt Integrate all the detailed instructions into a single, comprehensive prompt. For example: "First, identify the section of the text that needs improvement and explain why it is lacking. Next, summarize the current text in one sentence and list two specific weaknesses. Then, rewrite the text to address these weaknesses, ensuring the revised version includes three concrete examples, uses a formal and persuasive tone, and is structured as a numbered list with each item containing 2–3 sentences. Each list item must include at least one statistical fact or example, and the overall response must be no longer than 250 words. Before providing the final text, explain your reasoning step-by-step. If the revised text does not include at least two concrete examples, add an additional sentence with a real-world statistic." ___ Why This Works People never give enough context. And once ChatGPT answers, they never correct it enough. Think about it like an intern. Deep prompting is all about precision: give clear instructions, context & the right corrections. PS: Don't forget to use the new o3-mini model. It's crushing any other one. Yes – even DeepSeek.

Working with multiple LLM providers, prompt engineering, and complex data flows requires thoughtful organization. A proper structure helps teams: - Maintain clean separation between configuration and code - Implement consistent error handling and rate limiting - Enable rapid experimentation while preserving reproducibility - Facilitate collaboration across ML engineers and developers The modular approach shown here separates model clients, prompt engineering, utils, and handlers while maintaining a coherent flow. This organization has saved many people countless hours in debugging and onboarding. Key Components That Drive Success Beyond folders, the real innovation lies in how components interact: - Centralized configuration through YAML - Dedicated prompt engineering module with templating and few-shot capabilities - Properly sandboxed model clients with standardized interfaces - Comprehensive caching, logging, and rate limiting Whether you're building RAG applications, fine-tuning foundation models, or creating agent-based systems, this structure provides a solid foundation to build upon. What project structure approaches have you found effective for your generative AI projects? I'd love to hear your experiences.

🗃️ How To Write Better UX Research Reports (https://lnkd.in/eZ2aGwkU), a practical guide on how to structure findings from a UX research study more effectively, to be persuasive, engaging and use a story arc — from background and goals, to methods, activities, insights, findings and actionable recommendations. Neatly put together by Allison Grayce Marshall. I absolutely love Allison’s point that good UX research presents findings and insights, but doesn’t stop there. It helps teams understand what people said, why they said it and what implications those findings have on the product and the business. It explains behavior and persuades stakeholders. 🎯 Background and Goals First, we reflect the current state of things back to stakeholders. We explain who conducted research, the goals and scope of the project — both for current research activities and any research that will be conducted in the future. 🔬 Methods and Activities Next, we document how we uncovered insights, and why we chose specific methods and activities to do so. We showcase why insights are statistically significant, and whenever possible, highlight sample size needed for reliable surveys or interviews, confidence level and margin for error. We need it to build trust and confidence with stakeholders. 📣 Video Snippets and Verbatim Quotes Instead of using dense text paragraphs, we show video and audio clips, interview snippets and photos taken — not interpretations, but verbatim quotes, and direct observations. We tell the story that covers the journey a user went through, where they struggled and succeeded. In fact, we can use existing artefacts such as journey maps to visualize what exactly a user is going through, and what we've learned by observing these journeys. 🔮 Findings and Insights Next, we focus on key insights — ways of thinking, wrong assumptions, mismatch of expectations. We want to focus on 3–5 key findings that are striking, easy to remember and act upon. Bonus points if we can also connect them with risks, costs and missed opportunities. 🚀 Actionable Next Steps Finally, we wrap up with an overview of actionable steps to take, opportunities to explore and recommendations to follow. In that last step, we need to break down what our short-term objectives and action points are, and the strategy to accomplish long-term goals. That’s where we might mention next research projects, and how our insights and recommendations will flow back into the product development process. And every now and again, we might add an appendix with more details just in case you need to explain fine details of the project. As I was reading through the guide, I found myself nodding repeatedly. A helpful overview which is easy to follow, and covers all the key points that a good UX research report must highlight, and all the fine details that are often needed to make an impact in small and large companies. #ux #research