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EP 348: Large Language Model Best Practices - 7 mistakes to fix

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Manage episode 437131069 series 3470198
Conteúdo fornecido por Everyday AI. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Everyday AI ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Send Everyday AI and Jordan a text message

Win a free year of ChatGPT or other prizes! Find out how.
In today's episode, we're diving into the 7 most common mistakes people make while using large language models like ChatGPT.
Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Join the discussion: Ask Jordan questions on AI

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:
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14:59 Free chat GPT has outdated knowledge cutoff.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Large language models, training data, outdated information, knowledge cutoffs, OpenAI's GPT 4, Anthropics Claude Opus, Google's Gemini, free version of Chat GPT, Internet connectivity, generative AI,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.
Get more out of ChatGPT by learning our PPP method in this live, interactive and free training! Sign up now: https://youreverydayai.com/ppp-registration/

  continue reading

404 episódios

Artwork
iconCompartilhar
 
Manage episode 437131069 series 3470198
Conteúdo fornecido por Everyday AI. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Everyday AI ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Send Everyday AI and Jordan a text message

Win a free year of ChatGPT or other prizes! Find out how.
In today's episode, we're diving into the 7 most common mistakes people make while using large language models like ChatGPT.
Newsletter: Sign up for our free daily newsletter
More on this Episode: Episode Page
Join the discussion: Ask Jordan questions on AI

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:
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.
Get more out of ChatGPT by learning our PPP method in this live, interactive and free training! Sign up now: https://youreverydayai.com/ppp-registration/

  continue reading

404 episódios

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