Introduction to Large Language Models: A Deep Dive into LLMs and Their Applications

🔍Large language models (LLMs) are general-purpose models that can be pre-trained and fine-tuned for specific tasks.

💡Prompt engineering plays a crucial role in LLM development by creating prompts that improve performance and accuracy.

🎛️Parameter-efficient tuning methods allow for customization of LLMs without altering the base model.

💻Generative AI Studio provides developers with tools and resources to create and deploy LLMs.

🤖Gen AI App Builder enables the creation of Gen AI apps without coding, making it easy to build interactive and conversational applications.

What are large language models (LLMs)?

—LLMs are general-purpose models that can be pre-trained and fine-tuned for specific tasks, such as text classification, question answering, and document summarization.

What is prompt engineering?

—Prompt engineering involves creating prompts that improve the performance and accuracy of LLMs by providing domain-specific knowledge, desired output examples, and keywords.

What are parameter-efficient tuning methods?

—Parameter-efficient tuning methods allow for customization of LLMs without modifying the base model. Only a small number of add-on layers are tuned, which can be swapped during inference.

What tools are available for generative AI development?

—Generative AI Studio provides pre-trained models, fine-tuning capabilities, deployment tools, and a developer community. Gen AI App Builder allows for code-free creation of Gen AI apps. PaLM API enables testing and experimentation with LLMs and Gen AI tools.

How can I build interactive and conversational applications with LLMs?

—Gen AI App Builder allows you to create chatbots, digital assistants, custom search engines, knowledge bases, and more without coding. It leverages the power of LLMs to enable natural language interactions with the applications.

00:00Introduction to the course and the speaker.

00:08Overview of large language models (LLMs) and their significance in deep learning.

01:58Explanation of prompt engineering and its role in LLM development.

09:54Introduction to parameter-efficient tuning methods for customizing LLMs.

13:56Overview of Generative AI Studio and its tools for LLM development.

14:50Introduction to Gen AI App Builder and its capabilities for building code-free Gen AI applications.

14:57Overview of PaLM API and its role in LLM experimentation and application development.