This is the Everyday AI episode we probably shoulda done a while ago.... 👇 Because as different as ChatGPT, Gemini, Claude and others actually are under the hood, they have really started to copycat each other over the past 6 months. Which means we finally have a set of concrete best practices to get the best outputs from any LLM. Join us as we boil thousands of hours of experience into a 30-ish minute crash course that you can't afford to skip out on. 2026 LLM Cheat Code: 10 Essential Steps To Get the Most out of Any AI Chatbot -- An Everyday AI Chat with Jordan Wilson (Start Here Series Vol 26) Newsletter: Sign up for our free daily newsletter More on this Episode: Episode Page Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders. Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup Website: YourEverydayAI.com Email The Show: info@youreverydayai.com Connect with Jordan on LinkedIn Topics Covered in This Episode: LLM Landscape: Cookie Cutter Model Trends10 Essential Steps for AI ChatbotsChoosing the Right AI Operating SystemSelecting Optimal AI Chatbot SurfacesImportance of Paid AI Chatbot PlansUnderstanding LLM Context Window LayersContext Engineering and Prompt Best PracticesIntegrating Files, Apps, and Company DataAI Chatbot Privacy, Permissions, GovernanceTransparency, Observability, and Reasoning ArtifactsVerification, Iteration, and Workflow Automation Timestamps: 00:00 Keeping up with AI changes 03:55 Introduction to AI chatbots essentials 09:05 Rapid innovation in AI models 13:01 Understanding early AI models 14:37 Choosing an AI operating system 17:08 Discussing desktop app benefits 21:14 Understanding the context layer 23:55 Challenges without web search integration 28:55 Advancements in CRM connectors 32:35 Challenges with AI governance 35:13 Importance of observability in workflows 37:36 Developing universal AI skills Keywords: large language model, LLM, AI chatbot, AI operating system, ChatGPT, Claude, Gemini, Copilot, Perplexity, Grok, open models, cheat code for LLM, AI best practices, prompt engineering, context engineering, context window, context layer, reasoning models, generative AI, deterministic vs generative, web search in AI, model selection, paid AI model, free AI model risks, AI surface, desktop AI app, agentic capabilities, AI connectors, app integrations, business data privacy, permissions and governance, shadow IT, enterprise AI, observability, transparency, reasoning artifacts, workflow automation, verification loop, iteration in AI outputs, skill creation, plugin, automated workflow, agentic orchestration, company data security, expert driven loop, AI scheduling, context carry, modular AI, AI-powered work automation, personalized context, role-based access control, SaaS application integration, economic value of AI, knowledge work automation, prime prompt polish, refine queue, five five five framework, human-in-the-loop AI, knowledge cutoff, model versioning. Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)
