Exploring Language Models and Prompt Engineering in AI Projects

🤖 Machine learning includes chat gbt gpt4 and a large language model called Bloom.

🔗 Setting up a large language model in Google collab involves installing libraries and downloading a pre-trained model.

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

🔍 There are three different ways to use LLM: greedy search, beam search, and sampling top K and top P.

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

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

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

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

💡 Google's AI, Bard, demonstrates better understanding and provides sensible scientific answers.

📚 Prompt engineering guide teaches techniques for writing better prompts.

🔍 Different search techniques are used to determine sentiment from text.

🤔 Sampling top K and top p technique fails to determine sentiment accurately.

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

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

🔍 Different search methods, such as greedy search and beam search, can affect the performance of AI models.

🔑 The AI model is focused on word matching and lacks reasoning abilities.

📊 The AI model struggles with logic and pattern recognition tasks.

🔢 The AI model fails to identify the pattern of prime numbers in a sequence.

📚 Prompt engineering is an important discipline that involves creating effective prompts to solve problems.

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

💻 The video also mentions a prompt injection attack, which is similar to command injection.

📚 Using prompt injection to change the logic and understanding of models.

💡 Larger models perform better but require more memory.

👶 Explaining the concept of ignoring directions to a child.

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