Project Management

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.

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 ]

Avoiding tough talks is a direct path to losing team trust. Here's how top leaders handle conflict: 1/ The Real Problem → Leaders stall, hoping conflict resolves itself → Feedback gets softened until it’s meaningless → The issue festers, and performance suffers 2/ Why It Matters → Projects halt because no one says what needs to be said → The wrong people stay in the room, the right ones leave → Culture declines and misalignment becomes the norm 3/ The CLEAR Framework → Cut the Fluff: Skip the warm-up and get to the point → Label the Behavior: Focus on actions, not identity → Explain the Impact: Make it real, why does it matter? → Ask for Alignment: Invite a response, not a lecture → Recommit or Redirect: Don’t end vague, end with clarity 4/ What Happens Next → Tension goes down, not up → People feel respected, not ambushed → Projects move forward, with trust, not silence 5/ Why You Need This → Leading isn’t about avoiding discomfort → It’s about creating clarity when others won’t → This framework gives you the words to do it right What's your biggest takeaway?

Have I mentioned we are data geeks?🤓🤓 Performance uncertainty remains one of the biggest barriers to wider uptake of #energy #efficiency technologies.💡 #Wind-assisted propulsion,💨 air-lubrication systems🫧 and other proven #retrofits can cut fuel use by double-digit percentages.📉 But real-world savings swing with weather, routing and operations. Without clarity on a retrofit’s actual contribution, neither shipowners nor charterers can forecast returns with confidence.🤷🏻♀️ And because we’ve always believed that #data📊 can give us the clearest truth, we set out to address this challenge.👊🏻 Our friends at Eastern Pacific Shipping Pte. Ltd. gave us access to the Pacific Sentinel, on which we installed a high-frequency data acquisition system as three suction #sails⛵️ were retrofitted onboard the MR tanker in March 2025. Calibrated sensors captured #power consumption, vessel speed, engine load, heading and wind conditions every 15 seconds. Over four months as the vessel traded spot around the Americas,🌎 we saw #weather and #performance at a fidelity far beyond the single daily datapoint in a noon report. Building on #ITTC and DNV methodologies, Global Centre for Maritime Decarbonisation (GCMD) and EPS implemented an “on-off’’ testing protocol,🎛️ comparing power consumption with the sails activated and deactivated under otherwise similar environmental and operational conditions to isolate the sails’ true contribution. Under the predominantly near-headwind conditions sampled, the vessel saw an average instantaneous power savings⚡️ of 7.2%, with a 95% confidence interval between 6.2% and 8.2%. Instantaneous savings ranged from +28% to –14%. These rare outliers highlight just how sensitive power savings are to wind speed and direction, and underscore the importance of tracking dynamic operational data.⚠️ Access report here:  https://lnkd.in/g_dRFtJp If we want to scale energy-efficiency retrofits, we must tackle performance uncertainty head-on. Shipowners won’t invest, and charterers won’t commit, if they can’t trust that the #savings will show up in their fuel bills.💵 We therefore developed a power savings polar heat map to predict energy and fuel savings with wind conditions. With 3rd-party verification, this will enable performance-linked financing of the retrofits.💰 This case study is but a first step in building that validation layer. And it ladders🪜 up to what we launched last week: #FEET — the world’s first blended-finance fund designed to support energy-efficiency retrofits through a pay-as-you-save repayment structure. Progress is incremental, and this marks a big step in the right direction.👊🏻 Together, we are stronger; together, we can💪🏻 Shane Balani, Zheng Yang Cheng 钟正扬, Bhushan Taskar, Goh Wan Ni, Pavlos Karagiannidis, Mirtcho Spassov, CFA, Mike Wilson, Rashim Berry, Cyril Ducau

According to the 𝟐𝟎𝟐𝟒 𝐒𝐭𝐚𝐭𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐂𝐈𝐎 𝐒𝐮𝐫𝐯𝐞𝐲 by Foundry, 𝟕𝟓% of CIOs find it challenging to strike the right balance between these two critical areas. This difficulty is notably higher in sectors such as education (𝟖𝟐%) and manufacturing (𝟕𝟖%), and less so in retail (𝟓𝟒%). (Source: https://lnkd.in/ebsed9i7) 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐄𝐱𝐢𝐬𝐭𝐬: The increasing emphasis on digital transformation and artificial intelligence (AI) is driving the need for innovation. In 2024, 28% of CIOs reported that their primary CEO-driven objective was to lead digital business initiatives, a significant increase from the previous year. This push towards innovation often competes with the imperative to maintain operational excellence, including upgrading IT and data security and enhancing IT-business collaboration. 𝐓𝐡𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬: The tension between innovation and operational excellence can lead to a misallocation of resources if not managed correctly. It can result in either stifling innovation due to overemphasis on day-to-day operations or risking operational integrity by over-prioritizing disruptive technological advancements. For instance, sectors with a high focus on operational challenges, such as education and healthcare, particularly emphasize IT security and business alignment over aggressive innovation. 𝐀𝐝𝐯𝐢𝐜𝐞 𝐟𝐨𝐫 𝐂𝐈𝐎𝐬: • 𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐚 𝐃𝐮𝐚𝐥 𝐀𝐠𝐞𝐧𝐝𝐚: Get used to it! CIOs should advocate for an IT strategy that equally prioritizes operational excellence and innovation. This involves not only leading digital transformation projects, but also ensuring that these innovations deliver tangible business outcomes without compromising the operational integrity of the organization. • 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧 𝐈𝐓 𝐚𝐧𝐝 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Strengthening the collaboration between IT and other business units remains a top priority. CIOs should work closely with business leaders to ensure that technological initiatives are well-aligned with business goals, thereby enhancing the overall strategic impact of IT. • 𝐃𝐞𝐯𝐞𝐥𝐨𝐩 𝐚 𝐅𝐥𝐞𝐱𝐢𝐛𝐥𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐀𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥: To manage the dynamic demands of both innovation and operational tasks effectively, CIOs should adopt a flexible resource allocation model. This model would allow the IT department to shift resources quickly between innovation-driven projects and core IT functions, depending on the business priorities at any given time. ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

𝗧𝗼𝗱𝗮𝘆, 𝗣𝗠𝗜 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝘀𝘁𝘂𝗱𝘆 𝘄𝗲’𝘃𝗲 𝗲𝘃𝗲𝗿 𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗲𝗱 - 𝗼𝗻 𝗮 𝘁𝗼𝗽𝗶𝗰 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘁𝗼 𝗼𝘂𝗿 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻: 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘂𝗰𝗰𝗲𝘀𝘀. 📚 Read the report: https://lnkd.in/ekRmSj_h With this report, we are introducing a simple and scalable way to measure project success. A successful project is one that 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝘃𝗮𝗹𝘂𝗲 𝘄𝗼𝗿𝘁𝗵 𝘁𝗵𝗲 𝗲𝗳𝗳𝗼𝗿𝘁 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗻𝘀𝗲, as perceived by key stakeholders. This clearly represents a shift for our profession, where beyond execution excellence we also feel accountable for doing anything in our power to improve the impact of our work and the value it generates at large. The implications for project professionals can be summarized in a framework for delivering 𝗠𝗢𝗥𝗘 success: 📚𝗠anage Perceptions For a project to be considered successful, the key stakeholders - customers, executives, or others - must perceive that the project’s outcomes provide sufficient value relative to the perceived investment of resources. 📚𝗢wn Project Success beyond Project Management Success Project professionals need to take any opportunity to move beyond literal mandates and feel accountable for improving outcomes while minimizing waste. 📚𝗥elentlessly Reassess Project Parameters Project professionals need to recognize the reality of inevitable and ongoing change, and continuously, in collaboration with stakeholders, reassess the perception of value and adjust plans. 📚𝗘xpand Perspective All projects have impacts beyond just the scope of the project itself. Even if we do not control all parameters, we must consider the broader picture and how the project fits within the larger business, goals, or objectives of the enterprise, and ultimately, our world. I believe executives will be excited about this work. It highlights the value project professionals can bring to their organizations and clarifies the vital role they play in driving transformation, delivering business results, and positively impacting the world. The shift in mindset will encourage project professionals to consider the perceptions of all stakeholders- not just the c-suite, but also customers and communities. To deliver more successful projects, business leaders must create environments that empower project professionals. They need to involve them in defining - and continuously reassessing and challenging - project value. Leverage their expertise. Invest in their work. And hold them accountable for contributing to maximize the perception of project value at all phases of the project - beyond excellence in execution. 📚 Please read the report, reflect on its findings, and share it broadly. And comment! Project Management Institute #ProjectSuccess #PMI #Leadership #ProjectManagementToday

𝗙𝗼𝘂𝗻𝗱𝗲𝗿'𝘀 11-𝙥𝙤𝙞𝙣𝙩 𝗧𝗼𝗼𝗹𝗸𝗶𝘁 𝗳𝗼𝗿 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗥𝗶𝗴𝗼𝘂𝗿 Operating multiple businesses and investing in others has taught us invaluable lessons on driving operating rigour. Here's a 11-point toolkit for leaders to ensure execution excellence: 1. 𝑫𝒂𝒊𝒍𝒚 𝑲𝑷𝑰𝒔 𝑫𝒂𝒔𝒉𝒃𝒐𝒂𝒓𝒅: Automated D-1 report and intra-day metrics for high-velocity businesses published daily and hourly, respectively. 2. 𝑾𝒆𝒆𝒌𝒍𝒚 𝑭𝒊𝒏𝒂𝒏𝒄𝒊𝒂𝒍 𝑴𝑰𝑺: Maintain updated monthly trending P&L to track plan vs actual. 3. 𝑳𝒆𝒂𝒅𝒆𝒓𝒔𝒉𝒊𝒑 𝑴𝒆𝒆𝒕𝒊𝒏𝒈𝒔: Weekly 1-hour sessions to align on P&L trends for the month and solve gaps vs. plan. 4. 𝑴𝒐𝒏𝒕𝒉𝒍𝒚 𝑫𝒆𝒆𝒑 𝑫𝒊𝒗𝒆𝒔: 2-3 hours review of function-wise progress with <3 slides per team + last month’s P&L. 5. 𝑷𝒓𝒐𝒋𝒆𝒄𝒕 𝑹𝒆𝒗𝒊𝒆𝒘𝒔: 15-30 min weekly team stand-ups for critical projects (max 3). 6. 𝑳𝒆𝒂𝒅𝒆𝒓 1:1𝒔: Weekly (15 min) 1:1s with leaders working on multiple tactical projects with you; monthly (30 min) 1:1s with leaders working on long-term ones. 7. 𝑴𝒐𝒏𝒕𝒉𝒍𝒚 𝑻𝒐𝒘𝒏𝒉𝒂𝒍𝒍𝒔: Share wins, plans, and challenges transparently while celebrating top performers. 8. 𝑨𝒄𝒕𝒊𝒗𝒆 𝑻𝒆𝒂𝒎 𝑪𝒐𝒎𝒎𝒖𝒏𝒊𝒄𝒂𝒕𝒊𝒐𝒏: Use WhatsApp/Slack for project updates to keep teams aligned and energised. 9. 𝑹𝒆𝒔𝒑𝒐𝒏𝒔𝒊𝒗𝒆 𝒕𝒆𝒂𝒎 𝒎𝒆𝒎𝒃𝒆𝒓𝒔: Prioritise responsiveness over brilliance as an attribute in people you work with—it keeps everyone moving. 10. 𝑯𝒊𝒈𝒉 𝑯𝒊𝒓𝒊𝒏𝒈 𝑩𝒂𝒓: Never settle. Use recruiters, insist on detailed business case presentations, and personally vet references. 11. 𝑷𝒓𝒊𝒐𝒓𝒊𝒕𝒊𝒆𝒔: Keep your <10 priorities handy and impose discipline on yourself—add one priority only if you are willing to drop one. These practices help minimise distractions, maintain quality execution, and ensure teamwork. Hope it helps!

Risk is bad, isn’t it? Not always. Some risks are bad, but others you want to embrace. Why? Because they add value and allow you to serve your customers better. A little over a decade ago, in 2012, Robert S. Kaplan and Anette Mikes wrote a Harvard Business Review article “Managing Risks: A New Framework.” In this article they lay out a useful typology of three types of risk: Type 1: External Risk Definition: Risks outside your control, coming from external sources Examples: Climate change, recession, pandemic Mitigation: Reduce impact in case the event occurs  Tools: Scenario-planning, war games, stress-testing Type 2: Preventable Risk Definition: Risks arising from what happens within an organization Examples: accidents, mistakes, fraud Mitigation: Eliminate or prevent to minimize occurrence  Tools: Standard operating procedures, audits, norms and values Type 3: Strategic Risk Definition: Risks taken to create better strategic returns Examples: credit risk, R&D investments, location risk Mitigation: Reduce likelihood and impact in a cost-effective way Tools: Risk-maps, key risk indicators, Risk-based resource allocation In a nutshell: external risks you want to prepare for, preventable risks you want to avoid, and strategic risks you manage carefully. Of the three categories, I find Strategic Risk the most interesting type. Because, unlike the other two, it can add substantial value to a company and be an important part of its strategy. This means it comes with an interesting question: → Can we take on MORE risk to improve the performance of our organization? While seemingly unnatural from a risk management perspective, it’s more common than we might think. Because, taking over risk from your customers is a very common way of adding more value for them. Here’s some examples: - Any type of insurance - Any type of payment arrangement, especially no-cure-no-pay - Any type of leasing and renting model - Any type of X as a service approach To finalize, here’s a high-level risk approach based on the three types 1. List all the risks your organization faces 2. Categorize them in each of the three types 3. Reduce the possible impact of the external risks 4. Reduce the likelihood of the preventable risks 5. Investigate which strategic risks make sense to add 6. Manage likelihood and impact of strategic risks #riskassessment #forecasting #managementdevelopment

If I wanted to break into Data Analytics in 2024, these are the 4 types of projects and skills I'd include in my portfolio. A lot of people will tell you a magic formula-- "you need exactly 3 Tableau projects, 2 SQL projects, and an Excel project"🤡 I promise you, no one is out there looking to see Excel projects in portfolios LOL! Instead of focusing on checking boxes to complete the perfect formula of the right number of tools and projects, focus on the types of analyses to show the right quality of skills: 1. An EDA (exploratory data analysis) Skills Shown: investigation, problem solving, curiosity, data viz, developing insights and trends, communication 2. A Dashboard Skills Shown: data viz, developing KPIs and metrics, storytelling, answering business questions, pushing to prod 3. A Full Stack Project Skills Shown: Data cleaning & prep, ETL, data modeling, data viz, business recommendations, and storytelling 4. A Funnel Analysis Skills Shown: Data prep and wrangling, translating a business problem into data, project scoping, developing insights, making business recommendations to stakeholders, and storytelling (check out my payment funnel analysis in my course Solve Real-World Data Problems in SQL!) You could accomplish all 4 of these types of analyses in 1 big project. Or you could show them all separately. It doesn't matter how you do it. But whatever you do, stop taking courses and start doing THIS!⚡️ Happy Monday to all my BDEs⚡️💕

In 2011, the Amazon Appstore failed on launch and Jeff Bezos was furious. It was my fault, and I handled one aspect of recovery so poorly that one of my engineers quit. I still regret it 14 years later. Please learn from my mistake. The main lesson is that when you are leading through a crisis, it can feel like it is all about you. It isn’t. It is about: 1) Solving the problem 2) Guiding your team through it The product issue was that there were some pretty simple bugs, and we solved those problem well enough that I was eventually promoted. Where I failed was in guiding my team through the crisis. My leadership miss was that I neglected to encourage and support the engineer who had written the bad code. He did a great job stepping up and supporting the effort to fix the problem, but shortly afterward, he resigned. During the crisis, I failed to make clear to him that we did not blame him for the launch failure despite the bugs. I imagine that left room for him to think we blamed him or that he didn’t belong. It is also possible that others did blame him directly and that I was too caught up in the crisis to realize it. Both instances were my responsibility as the leader of the team. His resignation taught me a valuable lesson about leading through a crisis: No matter how bad the situation is, your team must be your first priority. If you make them feel safe, they will move heaven and earth to fix the problem. If you don’t, they may still fix the problem, but the team itself will never be the same. As a leader, here is how you can give them what they need: 1) Take the blame and do not allow others to be blamed. In some bug cases after this we did not release the name of the engineer outside the team in order to protect them from judgment or blame. 2) Separate fixing the problem from figuring out why it happened. Once the problem is fixed, you can focus on root-causing. This lowers the risk of searching for answers getting confused with searching for someone to blame. 3) Realize that anyone involved in the problem already feels bad. High performers know when they have fallen short and let their team down. As a leader you have to show them the path to growth and success after the crisis. They do not need to be beaten up on- they have taken care of that themselves. 4) See crises and problems as growth opportunities, not personal flaws. Your team comes with you in a crisis whether you like it or not, so you might as well come out stronger on the other side. As a leader, the responsibility for a crisis is yours in two ways: The problem itself and the effect it has on the future of the team. Don’t get too caught up in the first to think about the second. Readers- Has your team survived a crisis? How did you handle it?