Exploring Language Models and Prompt Engineering in AI Projects

This video discusses the setup of a large language model in Google Colab and explores different search methods and prompt engineering techniques for AI models. It also covers zero-shot and few-shot prompting and the concept of prompt injection.

00:00:00 This video discusses setting up a large language model in Google Colab and downloading a pre-trained model for machine learning projects.

๐Ÿค– Machine learning includes chat gbt gpt4 and a large language model called Bloom.

๐Ÿ”— Setting up a large language model in Google collab involves installing libraries and downloading a pre-trained model.

๐Ÿ’ก There are three different ways to generate text using the language model: greedy search, beam search, and sampling.

00:03:05 LLM Projects: Exploring different search methods for AI learning models and their effectiveness in answering questions. Greedy search, beam search, and sampling top K and top P are discussed.

๐Ÿ” There are three different ways to use LLM: greedy search, beam search, and sampling top K and top P.

โŒ Greedy search produces poor results, often repeating phrases and not answering the question effectively.

โœ… Beam search is a better alternative to greedy search, but its functionality is not clearly understood.

๐ŸŽฒ Sampling top K and top P is the most powerful method, providing optimal levels of randomness for better results.

๐Ÿ’ก The quality of results in LLM depends heavily on the prompt given, and better prompts can improve performance.

๐Ÿ’ก Google's AI, Bard, demonstrates better understanding and provides sensible scientific answers.

00:06:10 This video explores prompt engineering in AI text generation models and the different techniques used to improve prompt quality and accuracy.

๐Ÿ“š Prompt engineering guide teaches techniques for writing better prompts.

๐Ÿ” Different search techniques are used to determine sentiment from text.

๐Ÿค” Sampling top K and top p technique fails to determine sentiment accurately.

00:09:23 This video discusses different techniques for zero-shot prompting and few-shot prompting in AI models.

๐Ÿค– There are different ways to prompt AI models, such as zero-shot prompting and few-shot prompting.

๐ŸŽฅ Providing examples to AI models can yield accurate results, even if the examples are not logically correct.

๐Ÿ” Different search methods, such as greedy search and beam search, can affect the performance of AI models.

00:12:28 LLM Projects. Word matching, math, logic questions, pattern recognition. The AI struggles with reasoning but performs better with examples. Prime numbers are involved.

๐Ÿ”‘ The AI model is focused on word matching and lacks reasoning abilities.

๐Ÿ“Š The AI model struggles with logic and pattern recognition tasks.

๐Ÿ”ข The AI model fails to identify the pattern of prime numbers in a sequence.

00:15:33 The video discusses prompt engineering and demonstrates solving math problems using different models. It also introduces a prompt injection attack.

๐Ÿ“š Prompt engineering is an important discipline that involves creating effective prompts to solve problems.

๐Ÿ”ข The task involves solving math problems, such as calculating the total number of pencils or people in a given scenario.

๐Ÿ’ป The video also mentions a prompt injection attack, which is similar to command injection.

00:18:37 LLM Projects: Understanding the concept of prompt injection and the importance of crafting precise questions for accurate AI responses.

๐Ÿ“š Using prompt injection to change the logic and understanding of models.

๐Ÿ’ก Larger models perform better but require more memory.

๐Ÿ‘ถ Explaining the concept of ignoring directions to a child.

๐ŸŽฏ Importance of crafting well-formed questions for useful AI responses.

Summary of a video "LLM Projects" by Sams Class on YouTube.

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