EP 348: Large Language Model Best Practices - 7 mistakes to fix
Aug 30, 2024
Discover the seven common mistakes users make with large language models like ChatGPT. Learn why understanding model knowledge cutoffs is crucial for accuracy. Explore the importance of prompt engineering and how connectivity impacts outcomes. The discussion emphasizes transparency by cautioning against using screenshots for validation. Finally, find out why adopting AI tools is vital for businesses aiming to thrive in the future.
36:53
forum Ask episode
web_stories AI Snips
view_agenda Chapters
auto_awesome Transcript
info_circle Episode notes
volunteer_activism ADVICE
Knowledge Cutoffs
Understand large language models (LLMs) have knowledge cutoffs, meaning their data isn't always up-to-date.
Be mindful of this when using LLMs for time-sensitive information, as outdated data can lead to inaccuracies.
volunteer_activism ADVICE
Treat LLMs Like Traditional Research
Treat LLM research like traditional research; consider the data's timeliness.
If your project demands current information, don't rely on outdated LLM data.
insights INSIGHT
Model Cutoff Dates
Different LLMs have different knowledge cutoff dates.
OpenAI's GPT-4 is December 2023, Anthropic's Claude Opus is August 2023, and Google's Gemini is reportedly November 2023.
Get the Snipd Podcast app to discover more snips from this episode
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: 02:30 LLM knowledge cut-off 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trained with fresh, quality data crucial. 10:30 Daily use of large language models poses risks. 14:59 Free chat GPT has outdated knowledge cutoff. 18:20 Microsoft is the largest by market cap. 21:52 Ensure thorough investigation; models have context limitations. 26:01 Spread, repeat, and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.
Keywords: Large language models, training data, outdated information, knowledge cutoffs, OpenAI's GPT 4, Anthropics Claude Opus, Google's Gemini, free version of Chat GPT, Internet connectivity, generative AI,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.
Everyday AI Podcast – An AI and ChatGPT Podcast