EP 266: Stop making these 7 Large Language Model mistakes. Best practices for ChatGPT, Gemini, Claude and others
Everyday AI Podcast – An AI and ChatGPT Podcast
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In today's episode, we're diving into the 7 most common mistakes people make while using large language models like ChatGPT. Newsletter (and today's click to win giveaway): Sign up for our free daily newsletter)**More on this Episode: **Episode Page)**Join the discussion: **Ask Jordan questions on AI)Related Episodes:Ep 260: A new SORA competitor, NVIDIA’s $700M acquisition – AI News That Matters)Ep 181: New York Times vs. OpenAI – The huge AI implications no one is talking about)Ep 258: Will AI Take Our Jobs? Our answer might surprise you.)Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup)Website: YourEverydayAI.com)Email The Show: [email protected])**Connect with Jordan on **LinkedIn)**Topics Covered in This Episode:**1. Understanding the Evolution of Large Language Models
Connectivity: A Major Player in Model Accuracy
The Generative Nature of Large Language Models
Perfecting the Art of Prompt Engineering
The Seven Roadblocks in the Effective Use of Large Language Models
Authenticity Assurance in Large Language Model Usage
The Future of Large Language Models
**Timestamps:**00:00 ChatGPT.com now the focal point for OpenAI.
04:58 Microsoft developing large in-house AI model.
09:07 Models trained with fresh, quality data crucial.
10:30 Daily use of large language models poses risks.
14:59 Free chat GPT has outdated knowledge cutoff.
18:20 Microsoft is the largest by market cap.
21:52 Ensure thorough investigation; models have context limitations.
26:01 Spread, repeat, and earn with simple actions.
29:21 Tokenization, models use context, generative large language models.
33:07 More input means better output, mathematically proven.
36:13 Large language models are essential for business survival.
38:53 Future work: leverage language models, prompt constantly.
40:47 Please rate, share, check out youreverydayai.com.
**Keywords:**Large language models, training data, outdated information, knowledge cutoffs, OpenAI's GPT 4, Anthropics Claude Opus, Google's Gemini, free version of Chat GPT, Internet connectivity, generative AI, varying responses, Jordan Wilson, prompt engineering, copy and paste prompts, zero shot prompting, few shot prompting, Microsoft Copilot, Apple's AI chips, OpenAI's search engine, GPT-2 chatbot model, Microsoft's MAI 1, common mistakes with large language models, offline vs online GPT, Google Gemini's outdated information, memory management, context window, unreliable screenshots, public URL verification, New York Times, AI infrastructure.