Editing and Proofreading Skills

Different audiences need different openings. Here’s what works for each. There’s no one-size-fits-all opener. Your audience, executives, clients, and students, shape how you should begin. Here are 9 customized ways to hook attention based on who you’re talking to: 1. For Execs: Lead with a Promise    ➝ “In 7 minutes, you’ll see why this solution drives $3M in ROI.”     2. For Clients: Use a Vivid Visual    ➝ “Picture your Q3 roadmap—cut in half. That’s what this does.”     3. For Internal Teams: Tell a Story    ➝ “Two years ago, we faced the same challenge you’re in now…”     4. For Students: Ask a Provocative Question    ➝ “What if failing this test made you better at your job?”     5. For Pitches: Make a Bold Claim    ➝ “We’re not just solving X—we’re reshaping the category.”     6. For Workshops: Issue a Challenge    ➝ “Stand up if you’ve ever wanted to walk out of a training session.”     7. For Keynotes: Start with Silence    ➝ A pause before speaking builds gravity and presence.     8. For Tech Audiences: Hit with a Data Stat    ➝ “42% of teams still deploy weekly with manual QA…”     9. For Any Audience: Speak with Empathy    ➝ “I know how intimidating this can feel. I’ve been there too.”     The opener sets the emotional tone. Make it intentional. Who are you speaking to next? Pick one and practice. 📌 Save this cheat sheet 👤 Follow Jay Mount for communication systems that flex with context

I've been an editor for 7 years now. And here’s a truth bomb: 99% of editing advice online is generic. “Check grammar.” “Shorten sentences.” “Take a break.” Yes, but can we dig deeper? Today, I'm revealing the most underrated, unspoken editing hacks. No gatekeeping here: → Zoom Out to 50%: Sounds weird? Try it. Reducing text size makes formatting issues obvious. You’ll spot uneven line lengths and clunky layouts instantly. → Voice Note Test: Record yourself reading your draft aloud. Listen back without reading along. Awkward wording stands out painfully clear. → 'So What?' Technique: After every paragraph, ask “So what?” If there's no clear purpose—rephrase or remove. Keeps writing tight, engaging, purposeful. → One-Screen Rule: Keep each subheading's content fitting one screen. Scrolling mid-section causes reader fatigue. Break it down—short and crisp is key. → Color-Code Edits: Highlight different issues with different colors: 1) Pink for weak words (really, very, stuff). 2) Blue for unclear ideas. 3) Yellow for repetitive points. Visual cues speed up final revisions drastically. → Find-and-Replace for Punctuation: Search your commas, semicolons, dashes. Do you overuse them? Replace some with periods to punch up readability. → The Font Swap: Change your font temporarily. Your brain sees text as 'new' content. Mistakes and awkward phrasings jump right out. → Reverse Outline: Summarize each paragraph in 3-4 words. Is there logical flow? If not, rearrange or rework ruthlessly. Editing is surgery (don't question me). These hacks transform good content into remarkable content. But hey, I'm always learning. What's your top editing secret nobody talks about? Share it below 👇

Want to write like a CEO? Cut the fluff. The best leaders communicate with: ✅ Clarity ✅ Brevity ✅ Impact They don’t send long, rambling emails. They don’t hide behind corporate jargon. They get to the point fast. I have written four books and have advised 300+ CEOs on their communications. Here’s the 5-part writing framework top executives use: 1 – The Subject Line Should Say It All Before you write anything, ask: ➡️ What’s the ONE thing I need them to know? ➡️ What’s the ONE action I need them to take? If you can’t answer this, don’t send it yet. 2 – Lead with the Bottom Line Busy people don’t have time for long intros. 💡 Start with the main point, not the backstory. ❌ “Hope you’re doing well! I wanted to reach out because we’ve been working on…” ✅ “Here’s the update: [Key message in one line].” 3 – Cut the Fluff High-level executives don’t read wordy emails. They scan. ✂ Remove “just,” “I think,” and “wanted to.” ✅ “We should move forward.” ✅ “The results show a 20% increase.” 4 – Be Direct, Not Rude Great leaders are clear, not cold. 🚫 “Per our last discussion, I believe this approach might be beneficial.” ✅ “Let’s move forward with this approach. Thoughts?” 5 – Always End with a Clear Ask ❌ “Let me know what you think.” ✅ “Can you approve this by Thursday?” 6 – Add Warmth Charismatic people are both competent and warm. If you follow 1-5, you may come across as competent but it may be hard to connect. Therefore, add some warmth at the end. ❌ “Looking forward to your response.” ✅ “Appreciate your time on this—excited to hear your thoughts!” 📌 Follow me Oliver Aust for daily strategies on leadership communications.

Are you working in RAG and not getting better responses? 🤔 Chunking is one strategy you should be re-evaluating. 💡 As we strive to improve our Retrieval-Augmented Generation (RAG) models, it's essential to revisit fundamental techniques like chunking. 📚 But what exactly is chunking, and how can we leverage its semantic variant to enhance our results? 🤔 ---------------- What is Chunking? 🤔 Chunking is a natural language processing (NLP) technique that involves breaking down text into smaller, more manageable units called chunks. 📝 These chunks can be phrases, sentences, or even paragraphs. ---------------- What is Semantic Chunking? 💡 Semantic chunking takes this concept a step further by focusing on the meaning and context of the chunks. 🔍 This approach enables more accurate information retrieval, improved text understanding, and enhanced generation capabilities. ---------------- Here are three key aspects of semantic chunking: 📝 1️⃣ Contextual understanding: 🤝 Semantic chunking considers the relationships between chunks, enabling a deeper comprehension of the text. 2️⃣ Entity recognition: 🔍 This approach identifies and extracts specific entities, such as names, locations, and organizations, to provide more accurate results. 3️⃣ Inference and implication: 💭 Semantic chunking facilitates the identification of implied meaning and inference, allowing for more nuanced text analysis. ---------------- Why and where should you use semantic chunking? 🤔 1️⃣ Information retrieval: 🔍 Semantic chunking improves the accuracy of search results by considering the context and meaning of the query. 2️⃣ Text summarization: 📄 This approach enables the creation of more informative and concise summaries by identifying key chunks and their relationships. 3️⃣ Conversational AI: 💬 Semantic chunking enhances the contextual understanding of user input, leading to more accurate and relevant responses. ---------------- Comment below if you'd like to see video explanations on chunking strategies! 📹 Let's discuss how semantic chunking can elevate your RAG models and improve your NLP tasks! 💬 ---------------- Complete Blog: https://lnkd.in/gUE--eAJ Fixed Length Chunking: https://lnkd.in/gjNRd6Ni Sliding Window Chunking: https://lnkd.in/gEn4FW89 Hierarchical Chunking: https://lnkd.in/g_B3rrhd Sarveshwaran Rajagopal #SemanticChunking #NLP #RAG #InformationRetrieval #TextSummarization #ConversationalAI

Most "editing advice" over the internet is s**t You've heard it all before: "Take a break and then edit your content."  "Read your content out loud." "View it on a different device." Sure, those tips are good to start with but not to live with! Here is my 3-part editing process that covers everything you need to know - 1) Developmental editing 2) Copy editing   3) Proofreading I tackle them in that order - big picture stuff first, then zeroing in on the details. For the developmental edit, I evaluate: • Does this really answer what the reader wants to know?  • Does it accurately reflect my perspective/stance? • Are all the key points and arguments fully fleshed out? • Is the narrative structure and flow logical? • Is this catering to the right knowledge level? Then I move into copy editing mode to smooth out: • Paragraph transitions and flow • Use of active vs. passive voice • Removing redundancies  • Ensuring I've explained the "why" behind the "what" • Adding clear takeaways throughout Finally, I proofread with a picky eye for: • Spelling, grammar, awkward phrasing • Proper spacing and formatting of the posts The editor's mindset is moving from "this is good for the readers mostly" to "what's missing?" Following these 3 editing stages helps me catch all the big issues and polish the finer points. What does your editing process look like? I'd love to hear your tips and tricks!

Too many teams jump straight into content creation. “What should we post this week?” “Let’s try reels.” “Maybe a carousel will go viral?” Here’s what they’re skipping: A discovery process to deeply understand audience, culture & market A clarity phase to refine voice, brand identity & narrative A positioning layer to define UVP, category, and core promise A core concept that anchors all content And only then... content strategy, topics, formats & channels Most brands reverse the process. They start at the end and wonder why it doesn't scale. That’s why they: - Post inconsistently - Say the same thing as competitors - Burn time creating assets that don’t convert Content in 2025 needs systems. Not guesses. Here’s the loop I use (and build for clients): - Discover - Clarify - Position - Core Concept - Content Because real growth doesn’t come from random content. It comes from content that compounds. And the tools that help power it: Capture ideas: Semrush, ChatGPT, Brand24 Writing & refinement: Notion, Grammarly, (GPT) Visual design: Canva, Figma, Adobe Express, OpusClip Scheduling & publishing: Hootsuite, Planable, Sprout Social, Inc. ♻️ Repost it to share with your network. Follow me Madhav Mistry for insights on marketing

In my 14yrs career in engineering working for Big Tech companies such as Google and Uber, there is no other skill I used more than writing. And no, I don’t mean writing code. I mean English writing. Emails, Design Docs, Presentations, Feedback, Code Reviews, you name it. Here's how I make my written communication clear, effective, and punchy. 👇 Written communication can sometimes be daunting, especially for non-native speakers—like me. That’s why I wanted to share  the 6 questions that I use when writing anything. This helps me communicate more effectively and connect with my audience better. 1. Who is my target audience? Identify the specific group or individuals you are speaking to. Knowing your audience assists you in customizing your writing to meet their requirements and interests. 2. What is my main objective or purpose? Clarify the primary goal of your writing. Whether it's to inform, persuade, entertain, or educate, knowing your objective guides your content. 3. What key points do I want to convey? Identify the main idea or key points you want to communicate. This will help you stay focused and make sure your message is clear and logical. 4. Why should the reader care about this? Consider the value or benefit your writing offers to the reader. Highlight how it addresses their needs or solves a problem. 5. Is my writing clear, concise, and organized? Make sure your content is clear and easy to understand. Keep the flow logical and avoid using complex language or jargon that might confuse the reader. 6. Can I make my writing shorter? The answer is always yes. So make sure to edit edit edit. Brevity saves time for both the writer and the reader. What else would you add to this list? How does your writing process look like? ♻️ Please repost if you found this useful

Article from NY Times: More than two years after ChatGPT's introduction, organizations and individuals are using AI systems for an increasingly wide range of tasks. However, ensuring these systems provide accurate information remains an unsolved challenge. Surprisingly, the newest and most powerful "reasoning systems" from companies like OpenAI, Google, and Chinese startup DeepSeek are generating more errors rather than fewer. While their mathematical abilities have improved, their factual reliability has declined, with hallucination rates higher in certain tests. The root of this problem lies in how modern AI systems function. They learn by analyzing enormous amounts of digital data and use mathematical probabilities to predict the best response, rather than following strict human-defined rules about truth. As Amr Awadallah, CEO of Vectara and former Google executive, explained: "Despite our best efforts, they will always hallucinate. That will never go away." This persistent limitation raises concerns about reliability as these systems become increasingly integrated into business operations and everyday tasks. 6 Practical Tips for Ensuring AI Accuracy 1) Always cross-check every key fact, name, number, quote, and date from AI-generated content against multiple reliable sources before accepting it as true. 2) Be skeptical of implausible claims and consider switching tools if an AI consistently produces outlandish or suspicious information. 3) Use specialized fact-checking tools to efficiently verify claims without having to conduct extensive research yourself. 4) Consult subject matter experts for specialized topics where AI may lack nuanced understanding, especially in fields like medicine, law, or engineering. 5) Remember that AI tools cannot really distinguish truth from fiction and rely on training data that may be outdated or contain inaccuracies. 6)Always perform a final human review of AI-generated content to catch spelling errors, confusing wording, and any remaining factual inaccuracies. https://lnkd.in/gqrXWtQZ

A lesson for me and maybe for you. 👇 In cybersecurity we talk a lot about zero trust — but what we don’t talk enough about is about applying that mindset to information itself. Recently, I got caught out. Not by malware. Not by a phishing email. But by information that looked credible, and was shared by a trusted cybersecurity source. Sadly, it turned out to be inaccurate and misinformed. I don’t blame this person. As I said, it was a timely reminder to do better and to understand that: ✅ Trust is not a substitute for verification ✅ Cognitive bias affects all of us — even those trained to detect deception ✅ We all need to slow down and check. So, here’s my curated list of tools and resources to help spot misinformation, scams, and dodgy websites. I highly recommend taking a look — and please feel free to add others you trust in the comments. I’ll be checking them out! 😆 A course in how to find reliable info online: https://lnkd.in/e4rG8sfb Fact checker tools: https://www.factcheck.org/ https://lnkd.in/eUKBcRB6 StopagandaPlus (browser extension) https://lnkd.in/eJui5ijZ Tools like Full Fact, ClaimBuster, and Chequeado are at the forefront of automated fact checking. They cross-reference claims against databases of verified information, flagging potential falsehoods in near real time. However, they’re not infallible. These systems struggle with context, nuance, and rapidly evolving situations. They’re best used as a first line of defence, not as the final arbiter of truth. Check a website & find out how likely it is to be legitimate. Just put the url in and it will tell you: https://lnkd.in/eDSjP3S7 Ask Silver to check to see if a message is a scam. Upload a screenshot on WhatsApp and it will tell you & report it to the right authorities : https://lnkd.in/evG545Nn Virus Total (similar to check a website) https://lnkd.in/eYyhWMNU Can you detect these deepfakes? https://lnkd.in/ejf2c95U https://lnkd.in/e5etYRET ⸻ No matter how experienced you are, never let trust replace due diligence. Disinformation (fake news, deliberate spreading usually for a political agenda) and misinformation (mistake/ misinformed) are rife and scaling thanks to AI. Even the most well-intentioned sources can get it wrong. Stay curious, stay cautious, and keep learning. Got more tools or techniques you use to verify info? Share them below — let’s build better digital habits together. 💬👇 #CyberSecurity #Misinformation #MediaLiteracy #FactChecking #DigitalHygiene #CriticalThinking #ZeroTrust #Scams #OnlineSafety

In the rapidly evolving world of conversational AI, Large Language Model (LLM) based chatbots have become indispensable across industries, powering everything from customer support to virtual assistants. However, evaluating their effectiveness is no simple task, as human language is inherently complex, ambiguous, and context-dependent. In a recent blog post, Microsoft's Data Science team outlined key performance metrics designed to assess chatbot performance comprehensively. Chatbot evaluation can be broadly categorized into two key areas: search performance and LLM-specific metrics. On the search front, one critical factor is retrieval stability, which ensures that slight variations in user input do not drastically change the chatbot's search results. Another vital aspect is search relevance, which can be measured through multiple approaches, such as comparing chatbot responses against a ground truth dataset or conducting A/B tests to evaluate how well the retrieved information aligns with user intent. Beyond search performance, chatbot evaluation must also account for LLM-specific metrics, which focus on how well the model generates responses. These include: - Task Completion: Measures the chatbot's ability to accurately interpret and fulfill user requests. A high-performing chatbot should successfully execute tasks, such as setting reminders or providing step-by-step instructions. - Intelligence: Assesses coherence, contextual awareness, and the depth of responses. A chatbot should go beyond surface-level answers and demonstrate reasoning and adaptability. - Relevance: Evaluate whether the chatbot’s responses are appropriate, clear, and aligned with user expectations in terms of tone, clarity, and courtesy. - Hallucination: Ensures that the chatbot’s responses are factually accurate and grounded in reliable data, minimizing misinformation and misleading statements. Effectively evaluating LLM-based chatbots requires a holistic, multi-dimensional approach that integrates search performance and LLM-generated response quality. By considering these diverse metrics, developers can refine chatbot behavior, enhance user interactions, and build AI-driven conversational systems that are not only intelligent but also reliable and trustworthy. #DataScience #MachineLearning #LLM #Evaluation #Metrics #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gAC8eXmy