ChatGPT Usage Tips

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

“What AI skill should my team and I actually learn right now?” I will scream this from the rooftops of NYC. ➡️ Learn agent delegation Target a dedicated workflow or task. Assign an AI agent said role, define the outcome, set constraints, and schedule review gates. Treat it like a junior teammate and give it work, while monitoring so you can review for accuracy. Here’s my do-this-now stack, and how I’d run it with a team ⏬ If you’re a beginner: Start with ChatGPT Agent Mode. Open a new ChatGPT chat and change the dropdown to ‘Agent Mode’. It can plan tasks, execute steps, and return cited outputs for market scans, vendor comparisons, executive briefs, and decision memos. Kick off the job, let it run, WATCH IT RUN, and then review the completion. If you’re more technical or ops-heavy: Use Claude Code when the work requires operating UIs or your computer - clicking through portals, filling forms, wrangling spreadsheets, saving down documents. Expect more upfront setup and ownership, so keep a step-by-step prompt checklist, add automatic reruns for failing steps, and update the checklist only when the site’s labels or paths change. If you’re living in Google Workspace: Turn on Google connectors (Drive, Gmail, Calendar) inside ChatGPT or Claude. Ask the model to find your team’s file, summarize threads, compare document versions, prepare for and schedule meetings, or draft from past emails. This lets your agent pull context and act on it without manual hunting. How to turn this into outcomes in 30 days ⏬ → Twice a week, use Agent Mode to produce a one-page brief with citations and a recommendation on a real business question. Track cycle time and data/citation quality, and, where relevant, use Claude Code to automate in parallel. At the end of the month, you should know where a few agents can tackle real work and have the data to support what to scale. #AIinWork

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.

A few weeks ago I told my team that AI needs to do 92% of their work or they'll get left behind. Here’s how we're doing it (and why): Step 1: Get ChatGPT Plus/Pro Step 2: Create your master prompt • Tell AI: "I'm [your role] at [company type]. Create a master prompt for me. Ask me every question you need to give me the most context possible." • Spend 30-45 minutes answering everything it asks • Save the output as a PDF • Upload this to every new chat so AI knows your full context Step 3: Build system prompts Master prompts tell AI who you are. System prompts tell AI HOW to work. Here's the process: • Ask AI to create any output (email, ad, report) • Keep refining until it's perfect (3-6 iterations) • Then ask: "Write the system prompt that would have generated this output" • Save that prompt - it's now your intellectual property Now you have the exact formula to get that quality every time. Step 4: Use project folders  Think of these like rooms in your office with all context on the walls. • Create a project for each major area of your life/business • Upload your master prompt + all relevant documents • Every conversation builds on previous context • Share folders with your team for instant knowledge transfer I use this for investment decisions, business strategy, even family planning. Step 5: Set your custom instructions This makes AI remember how you like outputs formatted. Go to Settings → Personalization → Custom Instructions: • Tell it your communication style (short, bullet points, no fluff) • Remove AI language like "delve" and "moreover"  • Set your default tone and format preferences Never repeat formatting requests again. Step 6: Turn everything into custom GPTs These are your AI employees that do specific tasks consistently. • Take your best system prompts • Create custom GPTs for each repeatable task • Share them with your team • Update once, everyone gets the improvement I have custom GPTs for: emails, content creation, financial analysis, hiring, strategy docs. Step 7: Refine and improve Use AI to teach you AI. • Ask it to create your master prompt • Ask it to write your system prompts  • Ask it to suggest custom instructions • Ask it to help you build better prompts Here's what 92% actually looks like: - Content: AI does research, outlines, first drafts. You edit and add your voice. - Operations: AI creates SOPs, analyzes processes, suggests improvements. You decide. - Finance: AI analyzes reports, creates models, finds insights. You make decisions. - Strategy: AI processes information, suggests options. You choose direction. The 8% that stays human: Vision, taste, final decisions, and emotional intelligence. My team went from thinking AI was "kind of helpful" to saying it's their most valuable employee. It could be yours too. -DM P.S. If you want my complete prompting template and the 7 system prompts that save me 15+ hours per week, MESSAGE ME the word "AI" and I'll send it over. My gift to you 👊

STOP TELLING CHATGPT TO “MAKE IT BETTER” That’s not a prompt. That’s wishful thinking. Lazy commands get lazy results. If you want sharper writing, faster output, and pro-level strategy… Use these instead: 1. “Tighten this paragraph to make it punchier—without changing the meaning.” → Precision > polish. 2. “Rewrite this for [specific audience] who are scrolling with low attention.” → Context = conversion. 3. “Give me 3 new headlines that build curiosity, urgency, or emotional pull.” → Good hooks = good money. 4. “Break this wall of text into a skimmable, high-impact list.” → Format is leverage. 5. “Find the weakest sentence and rewrite it to hit harder.” → AI can be your editor. Use it that way. 6. “Change the tone to sound more confident and direct—like a founder pitching an investor.” → Voice shapes perception. 7. “Turn this into a 3-post series that teaches, builds trust, and drives engagement.” → Strategy > spam. You don’t need a better tool. You need better instructions. Prompt like a pro — and your results will start to look like it.

ChatGPT can help you land more job interviews. But only if you use it correctly. Try these 7 ChatGPT tips to cut your job search time in half (and boost the amount of interviews you land): 1/ Analyzing Job Descriptions ChatGPT can distill the most important aspects of a job for you. Just copy your target job description. Then paste it into ChatGPT and ask: "Highlight the 3 most important responsibilities in this job description: [Insert Job Description]" 2/ Tailor Your Resume ChatGPT can help personalize your resume for a target role. Just say: "Please personalize my resume for this [Job Title] role at [Company]. Here is the job description: [Paste Job Description]. And here is my resume: [Paste Resume]." 3/ Optimize Your Resume Bullets Take it one step further by optimizing your bullets. Copy a bullet from your resume. Paste it in ChatGPT and say: "Please rewrite this bullet in under 20 words using compelling language and measurable metrics from my resume: [Paste Resume]" 4/ Write Your Cover Letter ChatGPT can also help you write a highly personalized cover letter. Just say: "Please write a personalized cover letter for this [Job Title] at [Company]. Here's the job description: [Paste Job Description]. And here is my resume: [Paste Resume]." 5/ Prepare For Interviews (Part 1) ChatGPT can help you understand what companies are looking for in their interviews. Just ask it: "What specific skills and experience should I highlight in an interview for [Job Title] in [Specific Industry]?" 6/ Prepare For Interviews (Part 2) ChatGPT can also help you anticipate interview questions you might be asked. Just ask: "What are the 10 most common questions companies ask when interviewing a(n) [Job Title]?" 7/ Prepare For Interviews (Part 3) ChatGPT will also help you answer those same questions. Copy one of them and ask: "Draft an answer to the interview question [Insert Question]. The answer should use compelling language and include measurable outcomes." 8/ The 80:20 Rule of ChatGPT ChatGPT is not a replacement for you. Instead, let it do 80% of the baseline work for you. That way you save 100% of your energy for the most important parts: Editing, personalizing, and authenticating the information it gives you!

I’ve bombed so many interviews because I thought memorizing answers would make me sound prepared. Turns out I sounded like a robot reading from a script (who knew?) Then one night, after getting yet another rejection email, I knew I needed to change my strategy. I started using ChatGPT not to write my answers, but to help me practice telling my own story. Today, these are my 10 go-to AI prompts to nail all of my interviews: 👉 1. Practice real mock interviews ↳ Get custom questions that actually match your target role, both technical and behavioral. 👉 2. Generate role-specific questions ↳ AI creates questions divided into technical, behavioral, and situational categories for YOUR specific job. 👉 3. Build STAR Stories that sound like you ↳ Structure your experiences using Situation, Task, Action, Result. Without sounding rehearsed. 👉 4. Turn your resume into stories ↳ Identify your key achievements and transform them into confident, results-driven narratives. 👉 5. Explain complex stuff simply ↳ Learn to break down technical concepts for both technical and non-technical interviewers. 👉 6. Get honest feedback on your answers ↳ AI evaluates your tone, clarity, and structure, then helps you sound more natural and confident. 👉 7. Master the HR and behavioral rounds ↳ Test your emotional intelligence and communication for those culture-fit conversations. 👉 8. Create your personal 7-day prep plan ↳ Build a daily routine with mock questions, review topics, and reflection exercises. 👉 9. Customize Answers for Each Company Align your responses with specific company values, mission, and role expectations. 👉 10. Nail "Tell Me About Yourself" ↳ Craft an intro that connects your journey, skills, and goals to the role, in under 2 minutes. Interview prep isn't about having perfect answers memorized. It's about knowing your story so well that you can tell it naturally, no matter how they ask the question. ChatGPT should be your practice partner, not your scriptwriter. Try these prompts before your next interview. You might surprise yourself with how prepared you actually are 👏 ♻️ Reshare this for someone prepping for interviews and follow me for more AI and career tips!

Most people still prompt GPT-5.2 like GPT-4. The misconception: “Smarter model → less work on prompting.” With GPT-5.2, it’s the opposite. The model is more capable, but also more programmable. If you don’t lean into that, you leave reliability, latency, and trust on the table. You don’t need fancier prompts. You need prompt contracts: small, reusable spec blocks you standardize across your workflows. At minimum, you want these 5 written down: 1️⃣ Output & verbosity spec Define exactly how you want responses to look. > Default length (e.g. 3–6 sentences, or ≤5 bullets) > Special rules for simple vs complex tasks > How to format changes, risks, next steps This alone cuts noise, back-and-forth, and review time. 2️⃣ Scope & constraints Prevent scope creep before it happens. > “Do ONLY what was requested” > “No extra ideas unless asked” > “Align to existing guidelines, don’t invent new ones” You’re telling the model what not to do, which matters more than you think. 3️⃣ Long-context handling Long threads and documents are where things quietly break. > Skim and outline key sections before answering > Re-state user constraints (timeframe, audience, domain) > Anchor claims to specific sections instead of talking generically This turns “lost in the scroll” into predictable recall. 4️⃣ Uncertainty & hallucination guardrails Don’t expect the model to “just be careful.” > Call out ambiguity explicitly > Offer 1–3 clarifying questions or labeled assumptions > For high-risk answers, add a quick self-check step You’re designing how the model behaves when it doesn’t know. 5️⃣ Tool & structure rules Most real value is in tools + structured output. > When to use tools vs internal knowledge > How to batch or parallelize calls to save time > JSON schemas with required vs optional fields > “Set missing fields to null, don’t guess” This is how you get repeatable, testable behavior instead of vibes. Once you treat these as shared contracts, a few things happen: - Model upgrades (like moving to GPT-5.2) stop breaking flows - Evaluation becomes meaningful because behavior is stable - New use cases reuse the same specs instead of reinventing prompts - You can treat failures as contract violations, not “the model is weird” If you use GPT for serious work, your job isn’t to write pretty prompts. It’s to design a small library of prompt contracts your whole team can lean on. If you don’t have these 5 written down yet, that’s the homework. Full guide here: https://lnkd.in/e48_XPQf ---- ♻️ Repost if your network needs to see this transformation ➕ Follow me (Basia Kubicka) for more AI insights 🔔 Subscribe to my newsletter for deep dives: https://air-scale.kit.com/ Opinions expressed are my own and do not represent the views, policies, or positions of my employer.

PMs use AI to fix resumes. Almost none use it to fix their stories. Here is how to turn ChatGPT into your interview coach. One of the biggest gaps I see with project managers is this: In interviews, they list features and tasks. But what hiring managers remember is stories and impact. The good news, you already have the raw material. You are just not packaging it like a story yet. Here is a simple, step-by-step way to use AI to turn your experience into clear stories for interviews. 1️⃣ Pick one project Choose a project you are likely to mention in interviews. Ideally, one that felt messy, challenging, or high-stakes. 2️⃣ Brain dump the facts Write everything in a quick list. Tools, deadlines, blockers, scope changes, stakeholders, outcomes. Do not worry about structure yet. 3️⃣ Use this prompt to shape the story Copy your bullet list, then ask: "Act as an interview coach. Turn these notes into a STAR story for a PM interview. Keep it under 2 mins. Use simple language. Here are my notes: [paste]." 4️⃣ Use this prompt to make the impact visible "Rewrite the Result section with clear business impact. Add numbers, timelines, or risks reduced using ONLY my real details." 5️⃣ Use this prompt to tailor it to a role "Adapt this story for a PM interview. Keep the facts the same. Adjust the framing. Here is the JD [paste JD]." 6️⃣ Use this prompt to tighten your delivery "Shorten this story. Keep the impact. Remove repetition. Make it sound conversational and natural." Now you are not just saying. "I managed timelines and stakeholders". You are telling a clear, confident story that shows how you think, lead and deliver. You already manage complexity for a living. Storytelling is just how you make that complexity memorable. Repost to help another PM turn tasks into stories that stick. Follow Jesus Romero for practical project manager career tips that actually work in the real world.

If you’re using ChatGPT, how you prompt makes all the difference. The latest GPT-4.1 guide is out - here are my highlights. GPT-4.1 brings some 𝗺𝗮𝗷𝗼𝗿 𝗰𝗵𝗮𝗻𝗴𝗲𝘀: • Instruction Following: More literal and precise responses • Tool Use: Better integration with APIs and functions • Long-Context Handling: Can process much larger documents (up to 1 million tokens) • Multi-Step Workflows: More effective for agent-like tasks and business logic These are my key prompting takeaways: 🔹 𝗕𝗲 𝗱𝗶𝗿𝗲𝗰𝘁 𝗮𝗻𝗱 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 The model follows instructions more literally than previous versions. A single clear sentence often fixes unintended behaviour. 🔹 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 GPT-4.1 is ideal for multi-step tasks. For agents, include reminders to: • Keep going until the task is solved • Use tools rather than guessing • Plan and reflect between steps 🔹 𝗧𝗼𝗼𝗹 𝘂𝘀𝗲 𝗶𝘀 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 Use OpenAI’s tool API directly - no need for workarounds. Clear tool names and concise descriptions improve results. 🔹 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴 Want the model to “think aloud”? Ask it to break down tasks step-by-step. This improves accuracy and depth, especially for complex problems. 🔹 𝗟𝗮𝗿𝗴𝗲-𝘀𝗰𝗮𝗹𝗲 𝗶𝗻𝗽𝘂𝘁 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 It can handle up to 1 million tokens, but performance depends on how you structure the input. Place instructions before the content and keep formatting clean and focused. 🔹 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Older prompts may not work the same. GPT-4.1 relies less on implied rules—be explicit about desired output, structure, and tone. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: GPT-4.1 is highly steerable - but only if you guide it precisely. Build structured prompts, plan agent workflows, and iterate often. Smart prompting is now a skill - and a competitive advantage. Opinions: my own, Source: GPT-4.1 guide 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg