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 newsletterMore on this Episode: Episode PageToday'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 lineupWebsite: YourEverydayAI.comEmail The Show: [email protected] with Jordan on LinkedInTopics 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 AutomationTimestamps:00:00 Keeping up with AI changes03:55 Introduction to AI chatbots essentials09:05 Rapid innovation in AI models13:01 Understanding early AI models14:37 Choosing an AI operating system17:08 Discussing desktop app benefits21:14 Understanding the context layer23:55 Challenges without web search integration28:55 Advancements in CRM connectors32:35 Challenges with AI governance35:13 Importance of observability in workflows37:36 Developing universal AI skillsKeywords: 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) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist.

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