Everyday AI Podcast – An AI and ChatGPT Podcast

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

Aug 30, 2024

Discover the seven critical mistakes people commonly make when using large language models. Learn how the evolution of these models impacts their effectiveness. Delve into the importance of prompt engineering and keeping data current for optimal results. Understand the limitations of knowledge cutoffs and how they can affect outputs. Plus, explore the future role large language models will play in business survival. This is a must-listen for anyone looking to harness AI effectively.

36:51

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Quick takeaways

Awareness of knowledge cutoffs in large language models is crucial, as reliance on outdated data can lead to inaccuracies in professional contexts.

Understanding the generative nature and context limitations of large language models facilitates better interactions and more effective prompt engineering.

Deep dives

Understanding Knowledge Cutoffs

Large language models (LLMs) have a knowledge cutoff, meaning they only incorporate data up to a certain date, which can lead to outdated or incorrect information. Users often overlook this aspect, resulting in reliance on information that may no longer be accurate, potentially causing issues in professional contexts. It’s crucial to be aware of the specific cutoff dates for each model, as this can affect the validity of the responses generated when inquiring about recent developments. By misunderstanding knowledge cutoffs, individuals risk producing reports or communications that contain inaccuracies, highlighting the need for diligent awareness of each model's limitations.

Exploring Common Mistakes in Using Large Language Models

2min

Common Pitfalls in Using Large Language Models

35min

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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:
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/

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