AI Tools for Content Creation

If you’re an AI engineer trying to understand and build with GenAI, RAG (Retrieval-Augmented Generation) is one of the most essential components to master. It’s the backbone of any LLM system that needs fresh, accurate, and context-aware outputs. Let’s break down how RAG works, step by step, from an engineering lens, not a hype one: 🧠 How RAG Works (Under the Hood) 1. Embed your knowledge base → Start with unstructured sources - docs, PDFs, internal wikis, etc. → Convert them into semantic vector representations using embedding models (e.g., OpenAI, Cohere, or HuggingFace models) → Output: N-dimensional vectors that preserve meaning across contexts 2. Store in a vector database → Use a vector store like Pinecone, Weaviate, or FAISS → Index embeddings to enable fast similarity search (cosine, dot-product, etc.) 3. Query comes in - embed that too → The user prompt is embedded using the same embedding model → Perform a top-k nearest neighbor search to fetch the most relevant document chunks 4. Context injection → Combine retrieved chunks with the user query → Format this into a structured prompt for the generation model (e.g., Mistral, Claude, Llama) 5. Generate the final output → LLM uses both the query and retrieved context to generate a grounded, context-rich response → Minimizes hallucinations and improves factuality at inference time 📚 What changes with RAG? Without RAG: 🧠 “I don’t have data on that.” With RAG: 🤖 “Based on [retrieved source], here’s what’s currently known…” Same model, drastically improved quality. 🔍 Why this matters You need RAG when: → Your data changes daily (support tickets, news, policies) → You can’t afford hallucinations (legal, finance, compliance) → You want your LLMs to access your private knowledge base without retraining It’s the most flexible, production-grade approach to bridge static models with dynamic information. 🛠️ Arvind and I are kicking off a hands-on workshop on RAG This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build. Here’s what you’ll learn: → How RAG enhances LLMs with real-time, contextual data → Core concepts: vector DBs, indexing, reranking, fusion → Build a working RAG pipeline using LangChain + Pinecone → Explore no-code/low-code setups and real-world use cases If you're serious about building with LLMs, this is where you start. 📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d

What are 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) 𝗦𝘆𝘀𝘁𝗲𝗺𝘀? Here is an example of a simple RAG based Chatbot to query your Private Knowledge Base. First step is to store the knowledge of your internal documents in a format that is suitable for querying. We do so by embedding it using an embedding model: 𝟭: Split text corpus of the entire knowledge base into chunks - a chunk will represent a single piece of context available to be queried. Data of interest can be from multiple sources, e.g. Documentation in Confluence supplemented by PDF reports. 𝟮: Use the Embedding Model to transform each of the chunks into a vector embedding. 𝟯: Store all vector embeddings in a Vector Database. 𝟰: Save text that represents each of the embeddings separately together with the pointer to the embedding (we will need this later). Next we can start constructing the answer to a question/query of interest: 𝟱: Embed a question/query you want to ask using the same Embedding Model that was used to embed the knowledge base itself. 𝟲: Use the resulting Vector Embedding to run a query against the index in the Vector Database. Choose how many vectors you want to retrieve from the Vector Database - it will equal the amount of context you will be retrieving and eventually using for answering the query question. 𝟳: Vector DB performs an Approximate Nearest Neighbour (ANN) search for the provided vector embedding against the index and returns previously chosen amount of context vectors. The procedure returns vectors that are most similar in a given Embedding/Latent space. 𝟴: Map the returned Vector Embeddings to the text chunks that represent them. 𝟵: Pass a question together with the retrieved context text chunks to the LLM via prompt. Instruct the LLM to only use the provided context to answer the given question. This does not mean that no Prompt Engineering will be needed - you will want to ensure that the answers returned by LLM fall into expected boundaries, e.g. if there is no data in the retrieved context that could be used make sure that no made up answer is provided. To make it a real Chatbot - face the entire application with a Web UI that exposes a text input box to act as a chat interface. After running the provided question through steps 1. to 9. - return and display the generated answer. This is how most of the chatbots that are based on a single or multiple internal knowledge base sources are actually built nowadays. As described, the system is really just a naive RAG that is usually not fit for production grade applications. You need to understand all of the moving pieces in the system in order to tune them by applying advanced techniques, consequently transforming the Naive RAG to Advanced RAG fit for production. More on this in the upcoming posts, so stay tuned in! #LLM #GenAI #LLMOps #MachineLearning

Over the past year, Retrieval-Augmented Generation (RAG) has rapidly evolved—from simple pipelines to intelligent, agent-driven systems. This visual compares the four most important RAG architectures shaping modern AI design: 1. 𝗡𝗮𝗶𝘃𝗲 𝗥𝗔𝗚 • This is the baseline architecture. • The system embeds a user query, retrieves semantically similar chunks from a vector store, and feeds them to the LLM. • It's fast and easy to implement, but lacks refinement for ambiguous or complex queries. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Quick prototypes and static FAQ bots. 2. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗥𝗔𝗚 • A more precise and thoughtful version of Naive RAG. • It adds two key steps: query rewriting to clarify user intent, and re-ranking to improve document relevance using scoring mechanisms like cross-encoders. • This results in more accurate and context-aware responses. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Legal, healthcare, enterprise chatbots where accuracy is critical. 3. 𝗠𝘂𝗹𝘁𝗶-𝗠𝗼𝗱𝗲𝗹 𝗥𝗔𝗚 • Designed for multimodal knowledge bases that include both text and images. • Separate embedding models handle image and text data. The query is embedded and matched against both stores. • The retrieved context (text + image) is passed to a multimodal LLM, enabling reasoning across formats. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Medical imaging, product manuals, e-commerce platforms, engineering diagrams. 4. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 • The most sophisticated approach. • It introduces reasoning through LLM-based agents that can rewrite queries, determine if additional context is needed, and choose the right retrieval strategy—whether from vector databases, APIs, or external tools. • The agent evaluates the relevance of each response and loops until a confident, complete answer is generated. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Autonomous assistants, research copilots, multi-hop reasoning tasks, real-time decision systems. As AI systems grow more complex, the method of retrieving and reasoning over knowledge defines their real-world utility. ➤ Naive RAG is foundational. ➤ Advanced RAG improves response precision. ➤ Multi-Model RAG enables cross-modal reasoning. ➤ Agentic RAG introduces autonomy, planning, and validation. Each step forward represents a leap in capability—from simple lookup systems to intelligent, self-correcting agents. What’s your perspective on this evolution? Do you see organizations moving toward agentic systems, or is advanced RAG sufficient for most enterprise use cases today? Your insights help guide the next wave of content I create.

Is Using AI to Write LinkedIn Posts Right or Wrong? Let’s discuss.... Yesterday, I interviewed candidates for a role in my company. Part of the process involved a case study exercise. Imagine my surprise when 90% of the responses were eerily similar—some even word-for-word. It didn’t take long to realize they’d lifted content directly from ChatGPT, with no added thought, context, or originality. It got me thinking: in a world where AI tools like ChatGPT are so easily accessible, how do we maintain authenticity and originality, especially on platforms like LinkedIn where personal branding is everything? Let me tell you about a client I worked with recently. She wanted help building her thought leadership on LinkedIn. She’d been experimenting with AI to draft her posts. While the articles were technically sound, they didn’t carry her voice. There was no story, no spark, nothing that showed the uniqueness of her expertise. They read like well-polished reports, but they didn’t connect. And without connection, visibility and influence are hard to build. So, is it “wrong” to use AI? Absolutely not! But the key is how you use it. Here are some tips for Executives who want to use AI to create Thought Leadership content on LinkedIn that stands out while staying authentic: Overcome the Fear of the Blank Page: Many times the biggest hurdle is starting- AI can help. Use tools like ChatGPT to brainstorm ideas, draft outlines, or suggest titles. Think of it as your creative collaborator, not your replacement. Fact-Check Everything: A while ago I crosschecked some stats given by chat GPT and found some inaccuracies, especially in the interpretation. AI doesn’t always get it right. Always double check quotes, stats or industry-specific terms to ensure accuracy. Your expertise should always guide the narrative. Not the other way round Add Your Story: This is the special sauce- when you include your personal stories, anecdotes, experiences, insights, and voice onto the draft. A story only you can tell is what sets your thought leadership content apart. Refine for Your Voice: It can be tempting to let Chap GPT’s polished tone, takeover, but the magic is you. How do you want to sound? How do you want to show up? Do you want to be witty with a dash of professionalism? Tailor drafts so your voice and style runs through. While AI is a useful tool, it doesn't replace your years of experience and professional value- use it to refine your thoughts or drive creativity, but let your insights lead the way. Remember thought leadership is about sharing your unique perspective and connecting with others authentically. No AI tool can replace that. What do you think? Are you using AI for LinkedIn posts? How do you navigate authenticity in the age of AI? Let me know in the comments—I’d love to hear your thoughts! #Thoughtleadership #Executivevisibility #womeninleadership #AI

15 Ways to Use AI Like a Pro on LinkedIn (and Everywhere Else) (No, it’s not just for writing captions): Most people are using AI to save 5 minutes. Smart ones? They use it to unlock hours of creative energy. Here's how to do more of what matters without burning out: 1. Idea Expansion Prompt: “Give me 10 post angles on [topic] that feel fresh and bold.” → Great for breaking out of an echo chamber. 2. Content First Drafts Prompt: “Write a 3-part carousel post on [topic] with a hook, punchy bullets, and a CTA.” → Instant content base you can shape in your tone. 3. Thought Partnering Prompt: “Challenge the assumptions in this LinkedIn post.” → Use it to stress-test ideas before publishing. 4. Market Research Summaries Prompt: “Summarize what’s trending in [industry] for Q3 2025 using recent data.” → 5-minute briefs that replace hours of Googling. 5. Refine Voice & Style Prompt: “Rewrite this to match a tone that’s smart, sharp, and a bit witty.” → Feed your own writing to it to learn your own patterns. 6. Content Calendar Drafting Prompt: “Draft 5 post ideas for the next week that cover [pillar 1], [pillar 2], and [pillar 3].” → Batch content planning made effortless. 7. Headline Optimization Prompt: “Give me 7 alternative hooks for this post that stop the scroll.” → Headlines matter more than the body. 8. Comment Generator (That Doesn’t Sound Robotic) Prompt: “Write a short, smart, human-sounding comment to add to this LinkedIn post [paste post here].” → For staying visible, without writing forever. 9. Messaging Clarity Prompt: “Make this shorter, clearer, and easier to read at a glance.” → A clarity assistant on demand. 10. Voice-to-Writing Translation Prompt: “I’ll paste my raw voice note transcript. Make it into a carousel outline.” → For content that sounds like you, just more structured. 11. Outline to Post Flow Prompt: “Turn these 3 bullets into a structured LinkedIn carousel with transitions.” → Cuts your writing time in half. 12. Weekend Boundaries Prompt: “Can you schedule a post draft to review Monday morning and store it here?” → (Only if using integrated tools like Zapier + scheduling apps). 13. Inbox Triage Prompt: “Summarize this email thread and write a draft response based on my tone and priorities.” → Saves mental load after long weekends. 14. Repurposing Prompt: “Turn this newsletter into 3 post ideas with different formats (carousel, poll, quote post).” → Squeeze more out of what you’ve already made. 15. Quick Wins for Creator Burnout Prompt: “What’s a light, high-performing post I can make today with little effort?” → For the days when motivation is at 2%. Save this. You won’t remember them all. And the faster you start experimenting, the faster AI becomes your creative co-pilot. Which one are you trying next? ♻️ Repost if you know a content creator who’s tired of doing it all the hard way. ➕ Follow Helene Guillaume Pabis for smarter, sustainable strategies for visibility, content, and impact.

How is your DMO preparing for Google's latest changes to the search experience? I'm compiling a running list of tips - please share yours below! Content Structure ✔ Front-load Useful Content: Place key takeaways or bullet-point summaries at the top of your content. This helps users quickly find valuable information and improves engagement. ✔ Break Down Long-Form Content: Divide longer articles into shorter pieces, each answering a specific question. Focus on addressing topics rather than just targeting keywords. ✔ Include Titles and Feature Images: Use compelling titles and high-quality feature images to capture clicks from AI Overview results. Content Quality ✔ Create Content That Answers Complex Questions: Develop in-depth content that addresses complex queries, providing information that may not be easily answered by generative AI. ✔ Write New Content on New Ideas: Produce original content about new ideas and comparisons, especially for mid-funnel queries that existing online content may not cover. ✔ Include Sources, Quotes, and Stats: Use authoritative sources, quotes, and statistics to enhance the credibility and visibility of your content in AI search. Unique and Personal Content ✔ Include Personal Stories: Share personal experiences and stories to add a human touch that AI cannot replicate. This helps build credibility and engagement. ✔ Showcase Credible Experience: Highlight firsthand experiences and expertise to establish authority and trust with your audience. Alternative Traffic Channels ✔ Build Audiences in Push Channels: Develop and grow audiences in channels where you can directly push content to them, creating anti-fragile traffic sources. This includes CRM but could also mean increasing paid search and social budgets. ✔ Focus on Search Optimizations for YouTube: Optimize content for YouTube and other secondary search channels to reach a broader audience. ✔ Lean into Reddit: Their deal with Google means Reddit is frequently cited as a source for AI Overviews or in standard search results; the opportunity is both in responding across Reddit subs and in creating one, i.e. /r/VisitingOregon (h/t Mika Lepisto). ✔ Invest in Generative AI Chatbots: Implement generative AI chatbots to share and distribute content to visitors. #destinationmarketing

Stanley Made My LinkedIn Posts Take 3X Longer (And That's Actually Good!) A week ago, I became the first customer of Stanley, the new AI creative companion for LinkedIn by Vitalii Dodonov and John Hu (thanks guys!!) I thought AI might save me time on LinkedIn posts. But now I spend MORE time on them than ever Here's why that's the best thing that happened to my content... Before AI: 20 minutes to write a mediocre post After AI: 60 minutes crafting something I'm actually proud of (And performs much better! My first post with Stanley got 15,000 views) The difference? AI didn't replace my thinking - it amplified it. Here's what nobody tells you about AI for content: ✅ You'll generate 10X more ideas (and spend time choosing the best) ✅ You'll explore angles you never considered (and rabbit hole on research)   ✅ You'll polish every sentence (because now you have no excuse not to) I used to write one draft and hit publish. Now? I'm iterating like a software developer: Version 1: AI helps brainstorm Version 2: We refine the hook together Version 3: I add my personal stories Version 4: AI suggests structural improvements Version 5: I polish until it sings The paradox: AI gives you superpowers, but with great power comes... way more time perfecting your craft. Overall, my engagement is up 3X and I’ve really enjoyed having more, better conversations in the comments. Turns out, when you use AI as a creative partner instead of a shortcut, you don't save time. You invest it. You stop settling for "good enough" and start chasing "what's the best I can really do here?" So Stanley taught me this: the future isn't about AI making content creation faster. It's about AI making creators better. And that takes time. Beautiful, productive, game-changing time. Who else is spending MORE time on content since AI came along? 👇 - Rob (w/ Stanley) P.S. ♻️ Sharing is caring :)

How effective is Retrieval-Augmented Generation (RAG) in making AI more reliable for specialized, high-stakes data?  The BCG X team, led by Chris Meier and Nigel Markey, recently investigated the quality of AI-generated first drafts of documents required for clinical trials.   At first glance, off-the-shelf LLMs produced well-written content, scoring highly in relevance and medical terminology. However, a deeper look revealed inconsistencies and deviations from regulatory guidelines. The challenge: LLMs can not always use relevant, real-world data.   The solution: RAG systems can improve LLM accuracy, logical reasoning, and compliance. Team's assessment showed that RAG-enhanced LLMs significantly outperformed standard models in clinical trial documentation, particularly in ensuring regulatory alignment. Now, imagine applying this across industries 1️⃣ Finance: Market insights based on the latest data, not outdated summaries. 2️⃣ E-commerce: Personalised recommendations that reflect live inventories 3️⃣ Healthcare: Clinical trial documentation aligned with evolving regulations. As LLMs move beyond just content generation, their ability to reason, synthesize, and verify real-world data will define their value. Ilyass El Mansouri Gaëtan Rensonnet Casper van Langen Read the full report here:  https://lnkd.in/gTcSjGAE #BCGX #AI #LLMs #RAG #MachineLearning

AI is not here to replace you. It’s here to help you scale. As a coach, content fuels your business. But creating, repurposing, and managing it? That takes time—lots of it. This is where AI comes in. Not to take over, but to optimize. Here’s how to use AI to streamline your content: 1) Brainstorm unlimited content ideas Prompt: “Give me 10 LinkedIn post ideas for business coaches.” 2) Turn ideas into structured posts Prompt: “Write a LinkedIn post about [topic] in a conversational tone.” 3) Repurpose one post into multiple formats Turn it into: — A carousel (Summarize key points) — A tweet thread (Break it down into steps) — A video script (Make it engaging) Prompt: “Turn this LinkedIn post into a Twitter thread.” 4) Improve clarity & engagement Prompt: “Make this post more concise and compelling.” 5) Analyze and refine performance Use AI analytics tools to track: — Best-performing topics — Ideal posting times — Engagement trends The result? Less time creating. More time coaching. Better content that attracts ideal clients. AI doesn’t replace your voice. It amplifies it. PS: Want help scaling your business with AI? DM me and let’s optimize your strategy.

AI Overviews are here. Don't get buried—get featured. Your old SEO playbook won't cut it. To show up in AI-driven results, your content needs to be structured for machines and written for humans. Here’s how you can optimize your content to be the top choice for AI Overviews: 🧠 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗘𝗻𝘁𝗶𝘁𝘆 AI needs to know exactly what you're talking about. Clearly define key people, products, and concepts in your content and use Schema markup to label them. ❓ 𝗔𝗻𝘀𝘄𝗲𝗿 𝘁𝗵𝗲 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆 Structure your content to answer user questions head-on. Use clear H2s and H3s like "What is...?", "How to...", and "Why does...?" to signal direct answers. 🧑🏫 𝗕𝗿𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 (𝗘-𝗘-𝗔-𝗧) Don't just rehash what's already ranking. AI is getting smarter at identifying unique perspectives. Add first-hand insights, original data, or a personal story to stand out. 🤔 𝗔𝗻𝘁𝗶𝗰𝗶𝗽𝗮𝘁𝗲 𝘁𝗵𝗲 𝗡𝗲𝘅𝘁 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 Include a dedicated FAQ section that answers related sub-questions. This shows you've thought deeply about the topic and provides comprehensive value. 🔗 𝗕𝘂𝗶𝗹𝗱 𝗨𝗻𝘀𝗵𝗮𝗸𝗲𝗮𝗯𝗹𝗲 𝗔𝘂𝘁𝗵𝗼𝗿𝗶𝘁𝘆 AI Overviews heavily weigh trust. Earn high-quality backlinks and mentions from reputable sites in your niche to prove your authority. The bottom line: AI doesn't want random content; it wants the most authoritative, clear, and helpful answer. Be that answer. What's the #1 change you're making to your content for an AI-driven world? #SEO #AIOverviews #AIO #ContentStrategy #EEAT