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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:23 - 27 Nov 2024 -
A Crash Course on Using AI to Save Time and Focus on What Matters
A Crash Course on Using AI to Save Time and Focus on What Matters
Forwarded this email? Subscribe here for moreA Crash Course on Using AI to Save Time and Focus on What Matters
7 steps to level up your AI prompts and 8 of my favorite AI use cases for work
∙Guest post
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This is a guest post from my friend Peter Yang. Peter uses AI extensively to balance his full-time job, newsletter, and two kids, so I think you’ll love his practical tips:
Hey there! Peter here.
I've spent hundreds of hours experimenting with AI to save time at work. So today, I want to share an extremely practical guide with:
7 steps to craft a great AI prompt
8 of my favorite AI use cases at work
If you find this valuable, get my personal AI prompt library to help you save hours every week by copying what’s already working for me. Get 20% off the annual plan of my newsletter (until Black Friday) to unlock it and all my other AI guides.
Ok, enough Black Friday talk. Let’s dive in 👇
7 steps to craft a great AI prompt
Here’s how I usually adopt AI at work:
I start working on a super annoying and repetitive task.
I try to use 1-line prompts to get AI to do the work for me.
After struggling with lazy prompting, I finally decide to write a great prompt.
So, what makes a great AI prompt? Well, it usually includes these 7 steps:
Give AI a specific role and task: e.g., “You’re an expert editor…your task is to…”
Give specific instructions: e.g., “Follow these steps carefully…”
Include examples: e.g., “Analyze the <examples> to understand my style…”
Use XML tags for structure: e.g., “<data></data> and <prompt></prompt>.” Putting data before the instructions can improve AI’s response quality by 30%.
Give AI space to think (chain of thought): e.g., “In <thinking> tags, describe the key aspects of the edits you will make.”
Ask for variations: e.g., “Give me three variations.”
Add constraints: e.g., “Limit to 500 words.”
For example, here’s a prompt that I use to create PRDs from notes and discussions:
<reference> Paste in my PRD template here </reference> <instructions> You are a senior product leader and a clear written communicator. Your task is to help transform my rough notes into a great PRD. Please follow these steps carefully: 1. Analyze the <reference> to understand my desired style and format. In <thinking> tags, summarize the key characteristics of my PRD template. 2. Ask me to share my notes next. 3. Structure the PRD as follows: a) Problem: Clearly describe: Who is the customer? What is the customer's problem? How do we know that this is a problem? b) Goals: Include 1 goal metric and 2-3 input metrics. c) Solution: Clearly describe the solution and milestones. For each milestone, write concise user stories in first person view ("I see...", "I can...") with nested bullet points describing how the feature works. 4. Limit the PRD to 2-3 pages max 5. Present your response using: <draft_prd> tags for the structured document <follow_up> tags for follow-up items Ask me for more information if you need it. Be as clear, concise, and specific as possible. </instructions>If you only remember one thing from this section, make it this:
The most important step in writing a great AI prompt is to include examples.
Anyone can write a prompt to “make a viral social post” or “edit a newsletter.” But the magic happens when you show AI exactly what “viral” and “good” means.
That’s why I included my best examples in my personal AI prompt library:
Whenever I write a new AI prompt these days, I subconsciously include examples (along with XML tags, chain of thought, etc) to make it great. So keep these seven tips in mind as we explore my favorite AI use cases for work below.
8 favorite AI use cases for work
Since you’re reading ByteByteGo, I’m sure you’ve tried using AI coding tools like Cursor to ship faster. So today, I want to instead talk about how AI can solve some of the more annoying problems that we all run into at work:
1. Catch up on Slack threads
Nothing gives me a bigger headache than getting tagged on a 70+ message Slack thread with zero context. So, instead of reading the full thread, I use AI to summarize the takeaways, action items, and next steps:
<data> [Slack thread] </data> <instructions> You're a senior product leader who excels at synthesis. Your goal is to extract the key points from this Slack thread: - Takeaways - Action items and owners - Open questions Use quotes where relevant. Keep it under 250 words. </instructions>2. Summarize user feedback
I believe in building with customers, but manually parsing raw user feedback (e.g., Discord channels, forums, surveys) can take hours. Here’s a prompt that I use to get the insights in minutes instead:
<data> [Customer interviews/surveys/community posts] </data> <instructions> You're an experienced user researcher. Extract the following from this feedback: - Key pain points (with supporting quotes) - Prioritized list of feature requests Use nested bullets with clear headlines. </instructions>3. Learn new topics fast
My favorite way to learn a new topic is from a YouTube explainer video. But I’m too lazy to watch hour-long videos, so I use AI instead to get the takeaways instead:
Click “Show transcript” under a YouTube video
Copy and paste the transcript into AI with the following prompt:
<data> [Video transcript] </data> <instructions> You're amazing at extracting insights from long video transcripts. Transform the <data> transcript into a learning guide with: - Clear section headers - 20+ nested bullets - Include direct quotes where relevant Be extremely detailed and thorough. </instructions>P.S. This is also a great way to consume hour-long podcasts in 5 minutes.
4. Turn voice notes into documents
The more context you give AI, the better its output becomes. One of the best ways to give a lot of context quickly is to record a voice note and then get AI to summarize it:
Record a voice note with Superwhisper, ChatGPT Voice, or another tool.
Copy the full transcript and email it to yourself.
Paste it into AI with this prompt to clean it up:
<data> [Voice transcript] </data> <instructions> You're a talented writer who maintains voice while adding structure. Transform these thoughts into: - Clear main points - Logical flow - Supporting examples Give me three variations. Keep paragraphs short (2-3 sentences). </instructions>5. Make writing crystal clear
Many engineers struggle with written communication. I’ve seen otherwise smart engineers paste a wall of text into Slack that nobody wants to read.
So instead, paste your wall of text into AI first to make it more concise. The prompt below helps me edit everything from Slack messages to docs:
<data> [Text to make more concise] </data> <instructions> You're an expert editor focused on clarity. Make this more clear and concise: - Use simple language - Break into short paragraphs - Remove redundancy Give me three variations. </instructions>6. Write better documentation
Documentation often takes a long time to write. AI is great at writing a first draft that you can then edit later:
<data> [Code/API/Feature details] </data> <instructions> You're a senior engineer who writes exceptional docs. Create documentation that: - Starts with a clear overview - Includes practical examples - Covers error scenarios Include code samples. Keep it under two pages. Focus on what developers need to know. </instructions>7. Make a quick prototype
AI prototyping has come a long way. Here’s how I use it to make prototypes for my product ideas that I can then show to stakeholders and customers:
<instructions> You're an expert at building rapid UX prototypes. Please make a working prototype of the attached image. Here are the requirements: [Share high-level requirements or user stories in a list of bullets] </instructions>Keep iterating if AI doesn’t create what you want in one go. Here’s the website v0 made for me from a wireframe that I found online:
8. Solve problems
Let’s close with perhaps the most important tip of them all:
Think of AI as a coworker who is patient, knowledgeable, and available 24/7. Give it context and talk to it throughout the day.
I talk to it about everything from product strategy to navigating tough decisions to more (of course, you should only use company-approved AI if you’re sharing confidential information). You’ll be surprised at how helpful it can be!
Wrap up
Here are all 7 steps and 8 use cases again on a single screen:
I hope you’ll use this AI guide to save time with all the busy work so that you can focus more on the work that matters — talking to customers and shipping. If you’d like to explore more, consider getting 20% off my annual subscription to unlock my personal AI prompt library and all my AI guides.
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by "ByteByteGo" <bytebytego@substack.com> - 11:36 - 26 Nov 2024 -
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How can today’s executives lead from the head and the heart?
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Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
•
Timeless leadership. Geopolitical conflicts, aging populations, environmental disasters: the world faces a host of tough problems that require strong leadership. To alter the course of what the World Economic Forum (WEF) has deemed “a predominately negative outlook for the world over the next two years that [may] worsen over the next decade,” leaders need to step up. While times may change, the basic tenets of leadership—such as the importance of adaptability—will not, affirm Ida Jeng Christensen, who leads the WEF Forum for Young Global Leaders, and McKinsey Global Publishing’s Raju Narisetti in Forbes.
•
The power of empathy. In an era of complexity and constant change, the human aspects of leadership are increasingly important; today’s executives must lead from both the head and the heart. In their recent book, The Journey of Leadership, McKinsey senior partners Dana Maor, Kurt Strovink, and Ramesh Srinivasan and senior partner emeritus Hans-Werner Kaas call this “leading from the inside out,” a skill that begins with self-knowledge. Explore our road map to becoming a more human and authentic leader in The Journey of Leadership.
—Edited by Belinda Yu, editor, Atlanta
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A leader’s guide to the future of tech management
Leading Off
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Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Leading Off. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
Around the world, leaders of organizations are working to realize the massive potential value of generative AI (gen AI). From developing new products to boosting workforce productivity, the promise of gen AI extends to many corners of business. But across an organization, the roles most likely to be affected by the gen AI wave are those of tech executives. These leaders are being called upon to take on broader responsibilities for business performance while also weighing new risks as they consider the changes that will be necessary to build data- and AI-driven organizations. This week, we highlight the issues that tech officers and their organizations face as they seek to create value from gen AI.
The pressure is growing on tech executives, who are tasked with achieving the often-elusive goal of generating real value from gen AI and other technological innovations. McKinsey’s Aamer Baig, Jeffrey Lewis, Klemens Hjartar, and their coauthors have identified a set of shifts that tech officers can make to meet the growing demands of their bosses. These include assuming greater accountability for business outcomes, creating new digital- and AI-first businesses, being more proactive in protecting the enterprise from various threats, and expanding their leadership into areas outside IT. “In many cases, the pressure to move quickly leads to too much of a focus on the tech itself and not enough on everything else that’s required to make it work for an organization,” the authors say. “Creating significant gains from AI and tech means integrating multiple facets of the organization, such as adoption, business strategy, data, operating model, scaling, and talent.”
That’s the percentage of organizations that are regularly using gen AI in at least one business function—up from one-third in 2023, according to a 2024 McKinsey Global Survey on the state of AI. McKinsey’s Alex Singla, Alexander Sukharevsky, Lareina Yee, and their coauthors note that organizations are using gen AI most often in marketing and sales, product and service development, and IT. This finding aligns with previous McKinsey research showing that gen AI adoption in marketing and sales and in product and service development could generate the most value. The biggest increase in gen AI use is in marketing and sales, where reported adoption has more than doubled since 2023.
That’s McKinsey experts Alex Singla, Alexander Sukharevsky, Eric Lamarre, and Rodney Zemmel on the need to balance speed with precision to make the most of gen AI’s potential. This is harder than many leaders expect because deploying gen AI at scale means implementing broad changes to the ways their organizations fundamentally work. To start, leaders should determine the areas where gen AI can create true competitive advantages, identify the gen-AI-specific skills needed, and create a central team to scale up gen AI responsibly.
Gen AI can help power the finance team of the future to become “the home for business insights, driving the business to go bigger and faster,” OpenAI CFO Sarah Friar says in a recent episode of McKinsey’s At the Edge podcast. Friar tells senior partner Lareina Yee that she joined OpenAI, the company behind ChatGPT, to serve “in the crucible of the AI transformation.” Friar is leading by example, aiming to transform OpenAI’s finance function “from number crunching to being a better business partner.” Today, her department is using gen AI to streamline data collection, improve investor relations, and expand the team’s skill set. Five years from now, Friar says, “I want to be able to look at my team and see that everyone is in that mode of forward-thinking and insight-driven work.”
Gen AI is rewriting the script for many business functions, including the rules for picking your partners. McKinsey’s Alex Singla, Alexander Sukharevsky, Ben Ellencweig, and their coauthors say that strategic alliances with gen AI providers are vital, but treating them like traditional vendor arrangements won’t work. “The technology is still rapidly maturing, implementation is complex, and stability issues bedevil solutions,” the authors say. Important factors for forging strong gen AI partnerships include deepening trust and collaboration; finding multiple partners to meet the organization’s needs, since no one vendor can do it all; and avoiding overdependence on third-party capabilities.
Lead by creating lasting value from gen AI.
— Edited by Eric Quiñones, senior editor, New York
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by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 02:08 - 25 Nov 2024 -
What can fashion leaders expect in 2025?
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Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
•
A sluggish outlook. With the arrival of a long-feared cyclical slowdown, the fashion industry may experience a particularly turbulent 2025, McKinsey senior partner Gemma D’Auria and coauthors explain. At a time when consumers are increasingly price sensitive, the sector also faces the unexpected rise of dupe brands, the acceleration of climate change, and ongoing changes in the global trade environment. In short, fashion’s outlook for 2025 appears to be a continuation of the slow, single-digit revenue growth seen in 2024.
•
Strategies to win shoppers. To better reach consumers in 2025, executives told McKinsey that they will broaden their price ranges, localize their go-to-market models, and focus on brand positioning to attract value-seeking shoppers. In light of this, premium brands will need to do more to demonstrate why their products are worth the price. Download the full report, The State of Fashion 2025, for more on fashion’s ten critical themes for 2025, and register to join our State of Fashion webinar for attendees in Europe and Asia and in the Americas.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:46 - 25 Nov 2024 -
October Updated SOA 210827答复 Statement 220826
Greetings
Tried calling your colleague, but his number is not going through. As our MD Mr patrick is late i am the acting for now.
Attached updated October SOA till now, total USD 26162.21,
pls kindly check and advise your payment schedule for that, thanks!
OUR REF
H B/L NO.
M B/L NO.
ETD
ETA
AMOUNT ($)
20'
40'
HC
POL
POD
DN NO.
OFB210604126
PL2021066681
211593268
2024/3/5
12750.00
1
QINGDAO
ROTTERDAM
DB20210703900
OFB210800027
PL2021085186
OOLU2124741330
2024/4/12
14550.00
1
SHANGHAI
HAMBURG
DB20210807877
OAF210504431
PL2021065205
149105854629
2025/5/26
(1137.79)
1
NANSHA
HAMBURG
SFGD124829
Total:
26,162.21
Bank details:
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These industries could reshape the global economy
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What will the economy look like in 15 years? McKinsey’s Kweilin Ellingrud and coauthors believe that 18 arenas of competition could generate $29 trillion to $48 trillion in revenues by 2040. A recent McKinsey Global Institute (MGI) report describes these arenas of tomorrow and explores how they may materialize, including their potential sources of growth and dynamism. Check out the report and explore more insights on productivity, business superpowers, and trends in technology.
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by "McKinsey & Company" <publishing@email.mckinsey.com> - 03:14 - 24 Nov 2024 -
The week in charts
The Week in Charts
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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 03:53 - 23 Nov 2024 -
Is your digital strategy failing? Here’s why.
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Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of McKinsey Classics. We hope you find our perspectives useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
November 2024
In honor of the 60th birthday of McKinsey Quarterly, the firm’s flagship publication, the McKinsey Classics newsletter will be dedicated for a full year to classic Quarterly articles that were ahead of their time. Plus, we’ve refreshed Classics to better help readers connect these timeless insights to our latest, leading-edge thinking. Enjoy the changes—and be sure to sign up for a free McKinsey Quarterly digital subscription for special themed issues and bonus digital features, including compilations of Quarterly classics formerly available only in print..
The tech opportunities for today’s organizations are alluring. Businesses are racing to capitalize on the proliferation of technologies like generative AI, and with more data at their fingertips than ever, the potential to transform the business through tech seems vast. But companies looking to make digital hay need to play their cards right, otherwise they risk falling into the same traps that befuddled business leaders of yore have faced with earlier digital disruptions. That was certainly the story for the companies represented in this 2018 McKinsey Quarterly classic: only 8 percent of surveyed respondents said their companies’ current business model would remain economically viable through digitization.
What to do? Taking the right digital path means avoiding five critical missteps. The first is not having a clear vision of what digital really means and thus failing to connect a digital vision to that of the broader business. The second is not recognizing how digital is upending core economic principles. For example, customers are increasingly gaining value from digital, while companies’ profit pools are shrinking; the economic power curve is only getting steeper, making it more challenging to maintain market share; and above all, digital rewards fast movers—companies that develop a learning advantage quickly can outcompete their peers.
To learn about the other pitfalls that stymie companies’ digital efforts, read Tanguy Catlin and coauthors’ “Why digital strategies fail.”Read our latest thinking on this classic topic
Direct from Michael Dell: Leadership lessons and the future of AI
Dell chairman and CEO Michael Dell is no stranger to digital disruption—he’s been successfully sidestepping the digital snares of technological change for more than 40 years. Now he’s focused his attention on the latest trend, AI, which he says will transform the way organizations do business. To learn more about how companies can succeed in the face of digital disruption, read McKinsey senior partner Tarek Elmasry’s interview with Dell. And for additional insights on the future of technology and what that means for today’s organization, read the latest issue of McKinsey Quarterly.
— Edited by Drew Holzfeind, editor, Chicago
Is there a classic business topic you’d like us to feature? Send us an email—we’d love to hear from you.
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by "McKinsey Classics" <publishing@email.mckinsey.com> - 01:00 - 23 Nov 2024 -
EP139: Design a Live Streaming System
EP139: Design a Live Streaming System
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This week’s system design refresher:
API Pagination: Making Billions of Products Scrolling Possible (Youtube video)
Design a Live Streaming System
How NAT Made the Growth of Internet Possible
Must Know System Design Building Blocks
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API Pagination: Making Billions of Products Scrolling Possible
Design a Live Streaming System
Mike Tyson vs. Jake Paul was live-streamed on Netflix last week.
Let’s break down the typical tech stack of a live-streaming system.
Live streaming is challenging because the video content is sent over the internet in near real-time. Video processing is compute-intensive. Sending a large volume of video content over the internet takes time. These factors make live streaming challenging.
The diagram below explains what happens behind the scenes to make this possible.
Step 1: The streamer starts their stream. The source could be any video and audio source wired up to an encoder
Step 2: To provide the best upload condition for the streamer, most live streaming platforms provide point-of-presence servers worldwide. The streamer connects to a point-of-presence server closest to them.
Step 3: The incoming video stream is transcoded to different resolutions, and divided into smaller video segments a few seconds in length.
Step 4: The video segments are packaged into different live streaming formats that video players can understand. The most common live-streaming format is HLS, or HTTP Live Streaming.
Step 5: The resulting HLS manifest and video chunks from the packaging step are cached by the CDN.
Step 6: Finally, the video starts to arrive at the viewer’s video player.
Step 7-8: To support replay, videos can be optionally stored in storage such as Amazon S3.Join the Ultimate Streaming Data Event at Redpanda StreamFest! (Sponsored)
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How NAT Made the Growth of Internet Possible
Network Address Translation (NAT) is the process that has made the growth of the Internet possible.
But how does it work?
In a corporate or home setting, multiple devices (phones, computers, etc.) share one router with a single public IP address.
When a device wants to access the internet, it sends a request to your router. The request contains the device's private IP address.
The router’s NAT process replaces the private IP with the router’s public IP.
The modified request is sent to the internet.
When the response comes back, NAT checks its record and replaces the public IP with the correct private IP. It sends the response to the right device.
NAT has several important uses:
It helps conserve public IP addresses. Without NAT, IPv4 addresses would have been depleted much faster, severely limiting the growth of the Internet.
It allows sharing a single public IP address across multiple devices.
NAT acts as a basic firewall that hides internal IP addresses.
NAT also makes it easy to manage large networks.
Over to you: What else would you add to better understand NAT?
Must Know System Design Building Blocks
These are divided into 6 broad categories
Distributed Computing
Distributed message queues facilitate async communication and decouple services
Distributed caching improves performance by storing frequently accessed data in memory
A Distributed task scheduler manages and coordinates the execution of tasksScalability and Performance
Scaling services help adjust the capacity of services to handle changes in demand
CDNs serve content from geographically closer locations to improve performance and reduce latency.
Consistent hashing minimizes the remapping of keys when nodes are added or removedService Management
Service discovery enables services to find and communicate with each other without hard-coding network locationsNetworking and Communication
DNS translates human-readable domain names into IP addresses
Load Balancer distributes incoming network traffic across multiple servers
API Gateway acts as a single entry point for a group of microservicesData Storage and Management
Databases store and manage structured data
Object storage helps store complex objects like images, videos, and documents
Sharding helps horizontally partition data across multiple nodes
Replication helps horizontally scale the database by copying data to multiple nodesObservability and Resiliency
Gain insights into the system's internal state through metrics, logging, and tracing.
Over to you - Which other building block would you add to the list?
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by "ByteByteGo" <bytebytego@substack.com> - 11:54 - 23 Nov 2024 -
How can leaders protect the health of groups vulnerable to climate risks?
Only McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
•
Adapting to climate risks. Climate risks can affect a wide range of health conditions, people’s access to healthcare, and the health system’s ability to provide care, McKinsey senior partners Hemant Ahlawat and Matt Wilson and their coauthors write. In the face of these changes, adapting healthcare to meet the needs of kids, low-income earners, and other groups that are vulnerable to climate hazards is especially important. It’s a significant undertaking: McKinsey estimates that each year, there is a between $26 billion and $56 billion gap in investment in global health systems’ adaptation.
•
Pioneering medical products. According to an assessment of adaptation measures, further innovation in medical products and technologies could help to tackle climate-related threats. Examples include the development of new vaccines for diseases such as malaria and dengue, whose burden is increasing due to climate change. Consider three actions leaders can take to confront climate-related health risks and register to join McKinsey Sustainability and expert guests on COP’s closing day as they share perspectives on what was achieved at COP29 and what comes next.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:49 - 22 Nov 2024 -
Distributed Caching: The Secret to High-Performance Applications
Distributed Caching: The Secret to High-Performance Applications
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The demand for high-speed, high-performance applications has skyrocketed in recent years.
With users expecting real-time responses, especially in sectors like e-commerce, finance, gaming, and social media, even a few milliseconds of delay can lead to a poor user experience, potentially impacting customer satisfaction and revenue.
One core technique to accelerate data retrieval and improve application responsiveness is caching.
Caching works by temporarily storing frequently accessed data in a high-speed storage layer, often in memory. This allows applications to retrieve information faster than if they had to pull it from the primary database each time. A single cache node is often sufficient for smaller systems or applications with a limited user base to store and serve frequently requested data.
However, as systems grow, this setup faces limitations. Relying on a single-node cache to serve large-scale, high-traffic applications can lead to multiple problems.
This is where distributed caching comes into play.
Distributed caching involves spreading the cached data across multiple servers or nodes, allowing the cache to scale horizontally to handle large-scale applications. With a distributed cache, data is stored across multiple locations, meaning a single-node failure doesn’t compromise the entire cache, and the system can continue to serve requests seamlessly.
In this article, we’ll explore the concept of distributed caching in depth. We’ll look at how it works, discuss its key components, and examine common challenges and best practices for implementation.
Why Distributed Caching?...
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by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 21 Nov 2024
