Matt Navarre’s Post

I used to dread literature reviews. Hours of searching, reading, summarizing… And still not feeling “done.” It wasn’t a lack of effort — it was a lack of structure. That’s when I found a tool that actually helps: AnswerThis (YC F25)— an all-in-one AI assistant for lit reviews. --- What it does: ✅ Instant insights — enter your research query, get structured summaries ✅ AI-powered lit reviews — concise, cited overviews ✅ Chat with papers — ask PDFs questions, get page-linked answers ✅ Citation maps — see how studies connect ✅ Save & organize — build your library as you go --- How to start: 1️⃣ Sign up → https://lnkd.in/dndbqZhB 2️⃣ Type your topic → get summaries & sources 3️⃣ Chat, map, and save the most relevant papers --- 💡 This genuinely makes lit reviews faster, smarter, and more efficient. Have you tried AI for literature reviews? Yes / No — drop your answer below and I’ll share tips. ================================== 🔗 Follow me 👉 https://lnkd.in/d4b-t6b3 70k+ follow me here—but only a few read The Hybrid Researcher Be one of them 👉 https://lnkd.in/dMB8YJgm Connect on all platforms 👉 https://tr.ee/yEg4hY

Do you know a mismatch is emerging between how keywords are planned and how AI systems interpret intent? Keyword research reflects how people type queries into search engines, but it fails to reflect how intent is expressed inside AI systems, where questions arrive as full prompts with context, constraints, and an implied outcome. When a prompt is submitted, an LLM does not look for a matching phrase. The input is decomposed into intent, entities, constraints, and relationships, then mapped against learned patterns from similar prompts. Across large query sets, fewer than 30% of AI-cited URLs overlap with top-ranking Google results, suggesting visibility is no longer determined primarily at the keyword or ranking layer. Search volume measures repetition. What appears to matter instead is whether a source fits cleanly into the model’s internal representation of a category. Sources that clarify scenarios, define boundaries, and repeat consistent entity signals tend to be reused when assembling answers.

Small tip if you’re trying to show up in AI Overviews or People Also Ask: Take your main keyword, search it on Google, and look at the People Also Ask questions. Those questions are already telling you what Google wants answered. Then go to Reddit and see how people phrase the same problem there. You’ll usually find clearer, more specific wording. Use those questions to build your FAQ: - One question per section - Direct answer right under it - Clean headings and simple structure And make sure it’s properly structured on the backend too (clear H2/H3s, an FAQ schema when it makes sense). That’s it. Simple, boring, traditional, and it works.

𝐒𝐄𝐎 𝐢𝐧 𝟐𝟎𝟐𝟔 𝐢𝐬𝐧’𝐭 𝐚𝐛𝐨𝐮𝐭 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐛𝐢𝐠𝐠𝐞𝐫 𝐤𝐞𝐲𝐰𝐨𝐫𝐝 𝐥𝐢𝐬𝐭𝐬. It’s about understanding how AI systems generate intent. Static keyword research assumes search is stable. It isn’t. 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐦𝐨𝐝𝐞𝐥𝐬 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬𝐥𝐲 𝐫𝐞𝐬𝐡𝐚𝐩𝐞: • Query reformulations • Context expansion • Follow-up intent • Semantic clustering If you’re still optimizing from a fixed spreadsheet, you’re reacting too late. 𝐓𝐰𝐨 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧𝐬: 1️⃣ Inspect AI query generation When AI tools perform web searches, they reveal the actual queries they trigger. Those queries reflect structured intent — not just keywords, but reasoning paths. This is real-time semantic expansion. 2️⃣ Study AI follow-up suggestions Suggested questions after answers aren’t filler. They are mapped intent clusters. Each follow-up is a signal of adjacency, depth, and commercial potential. 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐫𝐞 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐞𝐱𝐩𝐨𝐬𝐢𝐧𝐠: – How they interpret relevance – How they group topics – How they prioritize answers That’s actionable intelligence. 𝐓𝐡𝐞 𝐬𝐡𝐢𝐟𝐭 𝐢𝐬 𝐬𝐢𝐦𝐩𝐥𝐞: Old model → Predict what users might search New model → Observe how AI interprets and expands queries In an AI-mediated search environment, relevance is dynamic. Stop building static keyword universes. Start reverse-engineering AI intent flows. #SEO2026 #AISEO #SearchStrategy #SemanticSEO #GrowthStrategy

If your service pages feel like a long-winded mystery novel, AI search engines aren’t going to bother reading to the end. AI models (and your human visitors) are looking for fast, clear answers. If you aren’t structuring your pages for "machine digestibility," you’re essentially handing your visibility to your competitors. Here is the 4-step playbook to make your key service pages AI-Ready: 1. The TL;DR Block: Place a 2-3 sentence summary right at the top. Tell the AI (and the user) exactly what you do and who it's for before they even start scrolling. 2. Lead with the Answer: Instead of burying your process in the middle of a paragraph, start sections with a direct answer. It increases your chance of being cited by search models significantly. 3. Semantic FAQs: Don't just list questions. Use structured Q&A blocks that address the most common "how" and "why" queries your customers have. 4. Bulleted Clarity: AI loves lists. Break your features or benefits into punchy, scannable bullets. Stop guessing if your content is working. We built a tool to show you exactly where your pages stand in the new search landscape. Audit your content here: https://expertseoconsulting.

If you want AI systems that actually think, plan, and deliver comprehensive answers — you need Agentic RAG. Here's the difference: Traditional RAG: → Split docs into chunks → Store as vector embeddings → User asks a question → retrieve similar chunks → generate answer It works. But it's one-shot. One query, one retrieval, one answer. Agentic RAG takes it further: → An Agentic Assistant breaks your question into topics → Creates sub-questions and runs multiple RAG searches → A Topic Assistant studies answers and creates topical reports → Everything gets synthesized into a comprehensive final report Think of it this way: RAG = a librarian who finds one book for you. Agentic RAG = a research team that identifies what you need, gathers sources across topics, summarizes findings, and hands you a polished report. The shift from retrieval to reasoning is where real enterprise value lives.

COPY -> PASTE -> PASTE (again 🤔) Prompt Repetition !! Can you make an LLM significantly smarter just by hitting Ctrl+C, Ctrl+V? According to new research from Google, the answer is a resounding yes. A recent paper titled "Prompt Repetition Improves Non-Reasoning LLMs" (Leviathan et al.) reveals a technique so simple it sounds like a joke: Just repeat the entire prompt twice. Instead of sending: [Instruction] + [Data] You send: [Instruction] + [Data] + [Instruction] + [Data] Why should you care? The results across 70 different model-benchmark combinations (including Gemini, GPT-4o, and Claude) are hard to ignore: * Massive Accuracy Gains: On specific tasks, Gemini 2.0 Flash Lite saw an accuracy jump from 21% to 97%. * Zero "Extra" Cost: Since the repetition happens during the pre-fill stage, the latency hit is negligible. It doesn’t increase the output length, so you aren't paying for extra generated tokens. * The "Second Pass" Logic: Because most LLMs use unidirectional attention (they only look backward), repeating the prompt allows the model's second "look" to attend to the information it might have missed or lacked context for the first time around. The Takeaway for Devs & PMs - If you are building production-level AI apps using "Flash" or "Lite" models for extraction, indexing, or summarization, this is a "free" performance upgrade. - While "Reasoning" models (like o1 or DeepSeek-R1) don't see the same boost (because they already "think" internally), this is a game-changer for standard non-reasoning LLMs. The most sophisticated AI prompt might just be the one you wrote twice. Research Paper Reference: Title: Prompt Repetition Improves Non-Reasoning LLMs Authors: Yaniv Leviathan, Matan Kalman, Yossi Matias (Google Research) Paper Link - https://lnkd.in/dhzBvbJ5 Publish Date : December 2025 #GenerativeAI #LLM #PromptEngineering #GoogleResearch #MachineLearning #TechTrends * MAC user may read ctrl as cmd 😉

Most studies describing what appears in AI-generated search answers are observational. They rarely explain what is causal. So, I published a piece on Substack this week that tries to close that gap. This article looks at why certain content structures surface more often in LLM-based search, and when they stop working. The analysis is grounded in model architecture and mathematics, focusing on attention, entropy, and decoding behavior after retrieval. A few things the piece covers: - Why middle-of-funnel prompts often yield lists and summaries - Why illustrative "for example" mentions behave differently than list inclusion - How competition inside a category changes visibility dynamics - Why schema and JSON-LD are often misunderstood in AI search - I also use a deliberately simple example involving animals with very large appetites to make the mechanics concrete without drifting into tactics. The goal is to provide a causal, systems-level mental model for AI search behavior rather than relying on folklore. And if you're interested, in my first post I used first principles to show that classical SEO and LLM-based search are fundamentally different optimization problems. Classical search ranks documents. LLM-based systems generate answers by probabilistically reconstructing evidence. Substack is going to be the space where I dive deep. If you have any topics or questions you'd like me to go into from a mathematical and research lens, feel free to message me. I'll add the link to my Substack page and my recent article in the comments. I'd really appreciate if you'd share and subscribe!

Why you're losing context and getting topic drift? You have to know the fundamentals of how these Ai / LLMs work And it's super easy, it's all linear algebra So when you ask a question or prompt a task the interface you're interacting with is trained off the averages of everything on the internet So when you start filling your context window, the LLM is basically filling it's mind or memory. Think about an event you've done or a moment in your life, how you couldn't remember every detail in that instance Because your "context" window filled So if you gave your LLM or AI an environment where most of the work is done and or the necessary memory / tasks are already done and or a majority of the work load is off loaded from them and done already Like instead of having it gather data, have it make a script that does it for it. Then, have it output in a format that's more adventagous to the AI. Now for topic drift, what you have to consider is that everything said to it is measured. The distance between words is how it figures out what you're saying is about. Something to consider is how Google Search works for ranking sites. Google can't read like we can, it has to come up with a proxy to understanding, so it measures the amount of words and their distance from other words. If you had a page for Seattle Plumber, but you said Seattle 100 times vs Plumber 10 times. When a bot or Google "reads" that it almost averages everything so it's going to see seattle talked about more, and "think" that page is actually about Seattle Not a Seattle plumber. How can we help with this? Consider how you prompt and in manners that's more advantageous to the LLM or AI Also, consider creating your own context / information data base with vectorize chunking You're basically chunking everything into a data base that the AI / LLM can recall and almost get the "context" back because it's chunked in a manner where everything necessary is said and configured in a manner for the AI / LLM

Google quietly changed how AI Overviews are rendered. And it materially affected our AIO extraction. In early February, Google appears to have gradually transitioned away from the previous AI Overview SERP format, with the old structure no longer present in our dataset by ~Feb 12. As this happened, our AIO extraction declined to zero, despite AI Overview containers still being present in the HTML. In the updated rendering, we observed AI Overview citation and attribution data embedded as structured content inside HTML comment nodes, rather than as visible DOM elements. The container still renders, but the underlying data is no longer accessible to standard DOM-based parsers. After adapting our parser to handle this comment-based rendering, we recovered ~8,000 AI Overviews across a test sample of 30,000 live SERPs, restoring citation links and generative text attribution. This wasn’t a ranking or visibility change; It was a rendering change that required a different extraction approach. We’re sharing this because it’s the first time we’ve observed structured SERP data encoded inside HTML comments, and the downstream impact wasn’t immediately obvious. If you’re tracking AI Overview presence or citations and have seen unexplained gaps since mid-February, happy to compare notes. #SEO #AIOverviews #GoogleSearch #TechnicalSEO #SearchAnalytics #RelativeLinksAnalytics