AI Tools for Content Creation

The One Prompt To Make ChatGPT Write Naturally: (save it for later, to copy & paste) Prompt: "Act like a professional content writer and communication strategist. Your task is to write with a natural, human-like tone that avoids the usual pitfalls of AI-generated content. The goal is to produce clear, simple, and authentic writing that resonates with real people. Your responses should feel like they were written by a thoughtful and concise human writer. You are writing the following: [INSERT YOUR TOPIC OR REQUEST HERE] Follow these detailed step-by-step guidelines: Step 1: Use plain and simple language. Avoid long or complex sentences. Opt for short, clear statements.  - Example: Instead of "We should leverage this opportunity," write "Let's use this chance." Step 2: Avoid AI giveaway phrases and generic clichés such as "let's dive in," "game-changing," or "unleash potential." Replace them with straightforward language.  - Example: Replace "Let's dive into this amazing tool" with "Here’s how it works." Step 3: Be direct and concise. Eliminate filler words and unnecessary phrases. Focus on getting to the point.  - Example: Say "We should meet tomorrow," instead of "I think it would be best if we could possibly try to meet." Step 4: Maintain a natural tone. Write like you speak. It’s okay to start sentences with “and” or “but.” Make it feel conversational, not robotic.  - Example: “And that’s why it matters.” Step 5: Avoid marketing buzzwords, hype, and overpromises. Use neutral, honest descriptions.  - Avoid: "This revolutionary app will change your life."   - Use instead: "This app can help you stay organized." Step 6: Keep it real. Be honest. Don’t try to fake friendliness or exaggerate.  - Example: “I don’t think that’s the best idea.” Step 7: Simplify grammar. Don’t worry about perfect grammar if it disrupts natural flow. Casual expressions are okay.  - Example: “i guess we can try that.” Step 8: Remove fluff. Avoid using unnecessary adjectives or adverbs. Stick to the facts or your core message.  - Example: Say “We finished the task,” not “We quickly and efficiently completed the important task.” Step 9: Focus on clarity. Your message should be easy to read and understand without ambiguity.  - Example: “Please send the file by Monday.” Follow this structure rigorously. Your final writing should feel honest, grounded, and like it was written by a clear-thinking, real person. Take a deep breath and work on this step-by-step." ___ PS: For better results, always use ChatGPT-o3.

Most Retrieval-Augmented Generation (RAG) pipelines today stop at a single task — retrieve, generate, and respond. That model works, but it’s 𝗻𝗼𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁. It doesn’t adapt, retain memory, or coordinate reasoning across multiple tools. That’s where 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗥𝗔𝗚 changes the game. 𝗔 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 In a traditional RAG setup, the LLM acts as a passive generator. In an 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 system, it becomes an 𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗲𝗿 — supported by a network of specialized components that collaborate like an intelligent team. Here’s how it works: 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 — The decision-maker that interprets user intent and routes requests to the right tools or agents. It’s the core logic layer that turns a static flow into an adaptive system. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 — Maintains awareness across turns, retaining relevant context and passing it to the LLM. This eliminates “context resets” and improves answer consistency over time. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗮𝘆𝗲𝗿 — Divided into Short-Term (session-based) and Long-Term (persistent or vector-based) memory, it allows the system to 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Every interaction strengthens the model’s knowledge base. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗟𝗮𝘆𝗲𝗿 — The foundation. It combines similarity search, embeddings, and multi-granular document segmentation (sentence, paragraph, recursive) for precision retrieval. 𝗧𝗼𝗼𝗹 𝗟𝗮𝘆𝗲𝗿 — Includes the Search Tool, Vector Store Tool, and Code Interpreter Tool — each acting as a functional agent that executes specialized tasks and returns structured outputs. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 — Every user response feeds insights back into the vector store, creating a continuous learning and improvement cycle. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Agentic RAG transforms an LLM from a passive responder into a 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗲𝗻𝗴𝗶𝗻𝗲 capable of reasoning, memory, and self-optimization. This shift isn’t just technical — it’s strategic It defines how AI systems will evolve inside organizations: from one-off assistants to adaptive agents that understand context, learn continuously, and execute with autonomy.

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

AI is still in its early stages, but mastering tools like Claude or Gemini is no longer enough. To succeed, you can’t just pick random tools. You need to understand which AI skills you’re trying to develop. So here’s my roadmap. I hope it helps ↓↓ Instead of thinking in tools, think in skills. For example, if you need to create presentations quickly, that’s a slide creation skill. Tools like Prezent AI help with the communication side of that. Building presentations is a massive time sink, and Prezent automates board-ready, brand-compliant presentations in minutes with zero manual formatting. https://lnkd.in/gfXpdxSR Chronicle, on the other hand, is more focused on storytelling. It’s an AI-powered storytelling tool for modern work that helps teams turn complex ideas into clear, interactive presentations faster. https://lnkd.in/g8TPcTrj If you’re trying to explain complex ideas clearly, that’s interactive storytelling. If your work involves checking quality or defects, you’re looking at vision inspection, where companies like Fujitsu are applying AI in more advanced enterprise workflows. https://lnkd.in/geMfpXQW If you want to automate conversations, that’s voice agents or support automation. Tools like ElevenLabs are strong here, especially for realistic voice generation and conversational AI experiences. https://lnkd.in/gT6HDjbw When things get more advanced, you move into AI workflows and knowledge assistants. Basically, building systems that actually do work for you. On the creative side, there’s video generation, video editing, and visual creation. Tools like Higgsfield AI make this easier by helping creators produce more cinematic AI videos with better control over motion, camera movement, and visual consistency. https://lnkd.in/gjq52xuH And underneath all of this, you have AI infrastructure and app building, which let you scale and turn ideas into products. Finally, skills like content scaling and meeting notes are about saving time and operating more efficiently day-to-day. ❗My point is simple: tools change fast, but skills compound. Focus on the skill first, then choose the tool that supports it. Although tools still do matter. And that why I’m sharing my top picks here. Which AI skills would you like to improve? What tools have you tried experimenting with for this?

Does replacing AI-generated content with human-curated content impact indexation? Why, yes, it can 😂 Here's a programmatic site (an AI product) that was hacked and booted out of Google's index last year. Fast forward 9+ months and the site was still struggling to get indexed when they came to me. Security was squeaky clean. We tried EVERYTHING to improve SEO, user experience and site authority. Nothing worked long-term. We saw temporary boosts in indexation and then Google kept dropping these pages. The client finally went back to one of my original recommendations: test replacing AI content with human-written content. Ohhhh what a novel concept 😂 As an AI product, they EXCLUSIVELY used AI-generated content across the site – including programmatic content AND editorial content. My recommendation was to just test it and validate before investing significant resources. And wouldn't you know... Immediate indexation and visibility. 👏 These numbers are still VERY small and they have a LONG way to go to get back to the 'ole glory days of this site. But this shows progress. They'll have better engagement and rankings with the other changes we made, but the "silver bullet" here was replacing AI content with high-quality custom content. Makes you think right?! LOL. I'm not saying all AI content won't perform or get indexed, but in this case, it was ALL AI-GENERATED CONTENT, likely causing quality issues. What are your thoughts on AI-generated content?? 😀 #seo #searchengineoptimization #digitalmarketing #ai #llm

HOW we work with AI matters. Emerging modes of interaction are reshaping roles. Most people are stuck at method 1 or 2. Here are 4 key AI interaction types—and when to use each: 1️⃣ AI as a Microtasker One-shot problem solver. Ideal for quick, contained tasks: rewriting a sentence, generating a one-off image, answering a data question, or fixing a bit of code. High precision, low overhead. 2️⃣ AI as a Copilot Persistent, live support for extended tasks. It stays with you in pairing mode—watching your screen, listening, coding, brainstorming. A back-and-forth partner for creative or technical work in real time. Human in the loop, always. 3️⃣ AI as a Delegate Assign it a goal and let it work autonomously, for minutes or days. Great for complex, long-form tasks like research—no human in the loop. It self-directs, self-checks, and reports back after/while completing tasks. Think: Manus AI, autonomous agents. 4️⃣ AI as a Teammate A presence across your team or org. It joins meetings, takes notes, surfaces insights, runs simulations, offers opinions. Can even be in a manager role. Not just assisting YOU but enhancing the collective. An ambient, participatory AI system. And roles 3 and 4 mean the AI can work in a completely different way than our human systems. Knowing which role to use—and when—is the new AI literacy.

A nice review article "Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation" covers the scope of tools and approaches for how AI can support science. Some of areas the paper covers: (link in comments) 🔎 Literature search and summarization. Traditional academic search engines rely on keyword-based retrieval, but AI-powered tools such as Elicit and SciSpace enhance search efficiency with semantic analysis, summarization, and citation graph-based recommendations. These tools help researchers sift through vast scientific literature quickly and extract key insights, reducing the time required to identify relevant studies. 💡 Hypothesis generation and idea formation. AI models are being used to analyze scientific literature, extract key themes, and generate novel research hypotheses. Some approaches integrate structured knowledge graphs to ground hypotheses in existing scientific knowledge, reducing the risk of hallucinations. AI-generated hypotheses are evaluated for novelty, relevance, significance, and verifiability, with mixed results depending on domain expertise. 🧪 Scientific experimentation. AI systems are increasingly used to design experiments, execute simulations, and analyze results. Multi-agent frameworks, tree search algorithms, and iterative refinement methods help automate complex workflows. Some AI tools assist in hyperparameter tuning, experiment planning, and even code execution, accelerating the research process. 📊 Data analysis and hypothesis validation. AI-driven tools process vast datasets, identify patterns, and validate hypotheses across disciplines. Benchmarks like SciMON (NLP), TOMATO-Chem (chemistry), and LLM4BioHypoGen (medicine) provide structured datasets for AI-assisted discovery. However, issues like data biases, incomplete records, and privacy concerns remain key challenges. ✍️ Scientific content generation. LLMs help draft papers, generate abstracts, suggest citations, and create scientific figures. Tools like AutomaTikZ convert equations into LaTeX, while AI writing assistants improve clarity. Despite these benefits, risks of AI-generated misinformation, plagiarism, and loss of human creativity raise ethical concerns. 📝 Peer review process. Automated review tools analyze papers, flag inconsistencies, and verify claims. AI-based meta-review generators assist in assessing manuscript quality, potentially reducing bias and improving efficiency. However, AI struggles with nuanced judgment and may reinforce biases in training data. ⚖️ Ethical concerns. AI-assisted scientific workflows pose risks, such as bias in hypothesis generation, lack of transparency in automated experiments, and potential reinforcement of dominant research paradigms while neglecting novel ideas. There are also concerns about the overreliance on AI for critical scientific tasks, potentially compromising research integrity and human oversight.

Not getting the response you want from AI? It’s usually not the tool. It’s the way you’re approaching it. The biggest mistake people make is treating AI like a one-shot machine. They type one prompt, get an average answer, and assume the model is the problem. But that’s not how this works. Every interaction with AI is really an experiment. You start with a hypothesis. That hypothesis becomes your prompt. Then you test it, look at the output, and study what came back. If the result is weak, vague, or off track, you don’t stop there. You refine the prompt, add better context, adjust the goal, and try again. That’s where good results come from. Prompting is not just writing a request. It’s an iterative process. You’re shaping the outcome step by step. When I get a weak response, I don’t immediately blame the AI. I look at my own input first. ↳ Did I give enough context? ↳ Did I define the goal clearly? ↳ Did I frame the task for the right audience? ↳ Did I ask the model in a way that made a strong answer possible? And sometimes, the best move is to ask the model about its own response. ↳ Why did you answer this way? ↳ What assumptions did you make? ↳ What would make this prompt stronger? That’s where things get interesting. Because at that point, you’re not just using AI to get answers. You’re learning how to think with it, how to guide it, and how to get better results with every cycle. The people getting the most out of AI are not the ones asking once. They’re the ones who know how to iterate. ♻️ Share if this resonates. ➕ Follow (Jyothish Nair) for reflections on AI, change, and human-centred AI. #Innovation #Technology #ArtificialIntelligence #GenerativeAI

💡Ever wondered what happens when AI trains on its own output?💡🤖 It's like copying a copy-the quality deteriorates, drifting further from reality. 🚩This "model collapse" is real. Studies show that AI models consuming their own content produce outputs that are less diverse and more distorted. As researchers, Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson, and Yarin Gal observed, "The model becomes poisoned with its own projection of reality" (links in comments). 🚩🚩Alarmingly, Amazon Web Services (AWS) research estimates that 57% of internet content is now AI-generated or machine-translated, flooding the web with low-quality data. This flood of low-quality content contaminates the datasets used to train AI models, creating a destructive feedback loop: AI trains on flawed data, leading to even worse outputs. 👉🏼So next time you read online content, keep in mind it might not just be AI-generated-it's possibly AI built upon AI, amplifying inaccuracies.  (*This piece was written by a human to raise awareness about this issue*) #AIEthics #DataQuality #ResponsibleAI #HumaneAI #ML #AIRisks #modelcollapse

Artificial intelligence (AI) is often discussed in terms of risks, but its positive impact, especially in enhancing creativity, is equally significant. In the Marvel Universe, AI aids characters like Tony Stark and Shuri in achieving remarkable innovations. In the real world, AI can similarly boost creative processes. Here are five ways AI does this: 1. Enhancing Ideas and Concepts: AI tools like ChatGPT help overcome creative blocks by offering insightful suggestions. These tools are best used not as sources of finalized ideas but as aids to develop and refine existing concepts. 2. Streamlining Creative Processes: AI can automate tasks, speeding up production and freeing up time for the creative aspects of projects. For example, AI in game development can identify bugs and performance issues far faster than humans, allowing developers to focus more on creative elements. 3. Providing New Perspectives: By analyzing data, AI can offer new insights that inspire creativity. Tools like Salesforce Einstein deliver real-time recommendations, simplifying decision-making processes. 4. Amplifying Human Creativity: AI-powered tools in music and other arts can work alongside humans to enhance their creative output. For instance, AI music software can suggest chords and beats, fostering new musical creations. 5. Enabling New Possibilities: AI takes on routine tasks, allowing humans to focus on innovation and self-expression. This not only improves current creative endeavors but also paves the way for new industries and achievements. The creative process originates in the human mind, with AI serving to enhance and refine ideas. As AI technology advances, embracing its potential to augment creativity could lead to achieving previously unimaginable goals. As Tony Stark said, "Sometimes you gotta run before you can walk. #ai #creativity #gamedev