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.
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Topics Covered in This Episode:
1. Understanding the Evolution of Large Language Models

2. Connectivity: A Major Player in Model Accuracy

3. The Generative Nature of Large Language Models

4. Perfecting the Art of Prompt Engineering

5. The Seven Roadblocks in the Effective Use of Large Language Models

6. Authenticity Assurance in Large Language Model Usage

7. 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14:59 Free chat GPT has outdated knowledge cutoff.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Large language models, training data, outdated information, knowledge cutoffs, OpenAI's GPT 4, Anthropics Claude Opus, Google's Gemini, free version of Chat GPT, Internet connectivity, generative AI,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chatbot model, Microsoft's MAI 1, common mistakes with large language models, offline vs online GPT, Google Gemini's outdated information, memory management, context window,

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