How to make AI give you better answers

Most people ask AI for the “right” answer. That’s why their outputs are flat, generic, and safe. Here’s the trick I use that feels almost illegal… 👉 First, force the AI to give you believable wrong answers. 👉 Then, have it correct itself... explaining why the right answer beats the fake ones. This “Chain of Lies → Chain of Truth” framework makes AI reason instead of guess. You’ll get sharper strategy, stronger copy, and insights that feel unfairly good. 💬 Comment “Illegal” below and I’ll send you my Prompt Engineering Master Guide with more frameworks you can swipe. #AI #PromptEngineering #BusinessStrategy #ContentMarketing #AICreators #AIAgents

Why does #AI sometimes “hallucinate”? Because it guesses. :) That’s where #RAG (Retrieval-Augmented Generation) changes the game. 🔹 Instead of guessing, RAG makes AI search first → then answer. 🔹 Think of it as ChatGPT + Google + Your Private Docs in one brain. 🔹 The result? Smarter. Accurate. Real-time AI. 💡 Real-world impact: *Customer support that answers from your own manuals *Healthcare bots that cite latest research *Finance assistants updated with new RBI guidelines > RAG connects AI’s intelligence with the world’s information. Are you exploring RAG in your projects yet? Share your thoughts 👇 #AI #GenerativeAI #RAG #LLM #ArtificialIntelligence #Innovation #MachineLearning

Practical AI tip #4: Make AI mark its own homework (so you don’t have to). Most “meh” AI outputs aren’t because AI is bad. They’re because we never told it what “good” looks like. Fix: give the model a simple rubric up front, then force a self-review & revision pass before it answers. How to do it: Define what “good” means (e.g. Accuracy, Specificity, Actionability, Tone, Brevity. 1–5 each). Set clear constraints: audience, length, voice, banned phrases, formatting, must-include details. Require a silent self-check: “Before you reply, score against the rubric; if any score <4, revise. Output final only.” Optional: ask for two cuts, 1. tight (executive-friendly), and 2. thorough (for doers). Why this works: Checklists reduce fluff. Rubrics create consistency. The self-review nudges the model to edit itself, so your V1s arrive closer to immediately useful. I’ve dropped a copy-paste rubric prompt in the first comment 👇 #AI #GenerativeAI #PromptEngineering #Productivity #WorkSmarter

How you split information determines what your AI “understands.” In RAG pipelines, documents are rarely ingested whole. They’re chunked into smaller units before retrieval. But here’s the catch: the way you chunk radically affects what the model sees. Common approaches: Fixed-size chunks (e.g., every 500 tokens) — simple, but can break meaning mid-sentence. Semantic chunking — split by sentences, paragraphs, or feature mentions to preserve meaning. Sliding windows — overlapping chunks to avoid missing context across boundaries. Adaptive chunking — dynamic chunk sizes based on content type (FAQ vs. review). A review like “Sound quality is great, but the ear pads hurt after an hour” — if chunked poorly, the insight is lost. With semantic chunking, it becomes two clear insights: Positive: Sound quality Negative: Comfort Is your AI pipeline chunking documents for convenience, or for meaning? #AI #RAG #ContextEngineering #AppliedAI #ArtificialIntelligence #GenerativeAI #SystemsEngineering #CXO #Leadership

Let's be honest : The real AI problem isn’t when models hallucinate but it’s when they give believable answers that stop us from asking questions. Hallucinations from AI are dramatic, obvious, and easy to ridicule. That’s why we fixate on them. But the real risk is the opposite: answers that sound polished, come with citations, and still steer us wrong. Those answers don’t trigger our doubts, they replace them. Practical things that can help: • Treat AI output like a first draft, not a decree. • Ask “How do you know that?” and demand clear sources. • Keep humans in the loop for decisions that matter. How do you make sure a convincing answer doesn’t become your blind spot? I’d love to hear one tactic you use. 👇 #TrustworthyAI #ResponsibleAI #ExplainableAI #CriticalThinking

⚡ AI feels effortless. But the “illusion of thinking” hides a heavy cost. 🌍 Gemini → 1 query = 9 sec TV + 5 drops water 🔥 GPT-5 → up to 40 Wh per response (💯× GPT-4o) As PMs, we face a choice: ✅ Route simple tasks to small models ⚡ Cache & optimize for complex ones ❌ Stop wasting heavy models on trivial asks 🔥 Use frontier models only when it truly matters 👉 The question isn’t what AI can do— It’s how responsibly we design it to scale. Read more in my blog👉 https://lnkd.in/gAvyH4ni #ProductManagement #AI #Sustainability #ResponsibleAI #TechLeadership 💬 Curious—how does your team think about efficiency vs capability in AI design?

AI isn’t just about accuracy, it’s about trust. In my latest article, I explore a question that doesn’t get asked enough: What role does Quality Assurance play in ensuring AI systems are reliable, fair, and explainable? https://lnkd.in/eXcUiFQd From data cleaning to bias testing to continuous monitoring, I share lessons learned from real data projects and why QA is the hidden guardrail that keeps AI safe and trustworthy. I would love to hear your thoughts: How do you approach QA in AI projects? Are we doing enough to test beyond accuracy? #AI #QualityAssurance #MachineLearning #DataScience #ResponsibleAI

𝐒𝐭𝐨𝐩! 𝐘𝐨𝐮'𝐫𝐞 𝐫𝐞𝐚𝐝𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐣𝐮𝐬𝐭 𝐥𝐢𝐤𝐞 𝐚𝐧 𝐀𝐈. As someone who deals with technology and AI on a daily basis, something I read a few weeks ago really surprised me: It turns out – and has been for decades, by the way – that we rarely read digital content from start to finish. Instead, most people just skim through a text, focusing on headlines, keywords, and eye-catching elements; which, by the way, is one reason why 👉 emojis are so popular in texts. (A note from the scientist in me at this point: there are some mixed study findings here; the impact this has on ultimate comprehension also depends on the type of text). Ironically, this reflects how today's AI applications process text: they search for key terms to extract the core meaning. But there is a difference: AI does not get bored, but it also cannot understand nuances or context at a human level unless explicitly instructed to do so. We increasingly make hasty judgments based on limited information – similar to AI that only receives selected keywords. Funnily enough, this limited view and hasty judgment is something we accuse AI of... The conclusion here is not to reject digital tools or efficiency. It's about knowing when deep reading is important (see also Erik Strauss's posts). Strauss MindTech #genai #reading #attention

Boardrooms now treat AI answers like word-of-mouth at scale. If you’re not in the answer, you’re invisible; if you are—with credible, high-quality content—perception and conversion shift fast. Content earns attention; quality builds trust; trust drives distribution and revenue. 👉 Full course + 1 hour workshop link is in the comments!! Marcel Santilli Jason Gong #ai #contentquality #gtm

What if the most important part of AI isn't the model itself? 🤔 A powerful AI can explain complex topics but can't remember your name from one conversation to the next. Why? Because the knowledge is in its training data, but the continuity is something we have to engineer. The real work is in designing the systems that give AI context and a sense of history. That's the difference between a one-off interaction and a truly intelligent tool. #linkedin #AI