Everyday AI Podcast – An AI and ChatGPT Podcast
EP 266: Stop making these 7 Large Language Model mistakes. Best practices for ChatGPT, Gemini, Claude and others
Description
The episode discusses common mistakes when using large language models, the future of work with these models, and the importance of knowledge cutoff dates, internet connectivity, memory management, and effective prompting techniques. It also highlights the investments being made by major companies in large language models.
Insights
Large Language Models and Knowledge Cutoff Dates
Different large language models have varying knowledge cutoff dates. Users need to be aware of these dates for accuracy and timeliness of information.
Internet Connectivity and Accuracy of Large Language Models
Internet connectivity plays a crucial role in the accuracy of responses from large language models. Using an internet-connected GPT can provide more accurate and up-to-date information.
Managing Memory and Context Window
Not managing the memory or context window of large language models is a common mistake. Different models have varying context windows.
Reliability of Screenshots and Prompting Techniques
Screenshots of large-language model outputs are not reliable indicators of performance. Large language models are generative and provide varied responses to the same prompt.
Effective Prompting Techniques and Future of Work
Few shot prompting consistently yields better results compared to one shot or zero shot prompting. Large language models are considered the future of work with significant investments being made by major companies.
Chapters
- Common Mistakes with Large Language Models
- Knowledge Cutoff Dates and Transparency
- Internet Connectivity and Accuracy
- Managing Memory and Context Window
- Reliability of Screenshots and Prompting Techniques
- Effective Prompting Techniques and Future of Work
- The Future of Work with Large Language Models
Common Mistakes with Large Language Models
00:01 - 07:18
- Using large language models like chat GPT, Microsoft co-pilot, and Google Gemini can lead to common mistakes.
- Apple is developing new chips for data centers to enhance AI processing efficiency.
- OpenAI is moving closer to launching a search engine to compete with Google.
- OpenAI has released new versions of the GPT-2 chatbot model.
- Microsoft is developing MAI-1, a large language model with around 500 billion parameters to compete with OpenAI.
Knowledge Cutoff Dates and Transparency
06:49 - 14:09
- Large language models have knowledge cutoff dates that may lead to outdated or inaccurate outputs.
- Different large language models have varying knowledge cutoff dates.
- Users need to be aware of knowledge cutoff dates for accuracy and timeliness of information.
- Transparency and trust regarding knowledge cutoffs are crucial for users relying on large language models.
Internet Connectivity and Accuracy
13:43 - 20:57
- Internet connectivity plays a crucial role in the accuracy of responses from large language models.
- Using an internet-connected GPT can provide more accurate and up-to-date information.
- Co-pilot from Microsoft demonstrates good performance in providing recent and accurate data on market cap rankings.
Managing Memory and Context Window
20:28 - 27:39
- Not managing the memory or context window of large language models is a common mistake.
- Different models have varying context windows.
- Sharing screenshots without providing the public URL for verification is misleading.
- Screenshots can be manipulated and do not reflect the actual capabilities of the model.
Reliability of Screenshots and Prompting Techniques
27:17 - 34:05
- Screenshots of large-language model outputs are not reliable indicators of performance.
- Large language models are generative and provide varied responses to the same prompt.
- Copy and paste prompts do not work effectively with large language models.
Effective Prompting Techniques and Future of Work
33:44 - 41:08
- The triple P method in artificial intelligence has been a game changer.
- Different prompting techniques have varying impacts on the quality of outputs.
- Few shot prompting consistently yields better results compared to one shot or zero shot prompting.
- Copy and paste prompts are not effective; proper prompt engineering is crucial.
- Large language models are considered the future of work with significant investments being made by major companies.
- Companies that fail to adopt large language models risk falling behind competitors.
The Future of Work with Large Language Models
40:44 - 44:17
- The future of work involves using large language models for knowledge work.
- Seven common mistakes with large language models include knowledge cutoff, internet connectivity, memory management, generative nature, and more.
- Large language models are crucial for the future of work and require proper understanding and utilization.