1 Deploying reliable and responsible large language models in the real world
This chapter covers
- Understand the capabilities and inner workings of large language models (LLMs).
- Explore the diverse applications of LLMs across industries.
- Learn key considerations for reliable and responsible deployment.
- Recognize the critical challenges, such as LLM hallucination risks, efficiency, bias, and optimization of performance, and how to mitigate them effectively.
Large language models (LLMs) are triggering an AI revolution that is reshaping how we create and consume content. Powered by ever-growing computational capabilities, LLMs like GPT and Claude have attained remarkable human-like proficiency in generating text. Their versatile linguistic skills are catalyzing groundbreaking innovations across industries.
LLMs are automating rote content creation, freeing up humans for more creative and strategic work. For instance, an LLM can rapidly synthesize structured earnings reports or draft routine emails to free up analysts and assistants. Marketing teams use LLMs to effortlessly customize messaging at scale. LLMs even create first drafts of code, webpages, stories and more for developers, saving them repetitive work.
Intelligent assistants built using LLMs can book appointments, facilitate complex transactions and deliver on-demand information. LLMs are also enhancing human capabilities - lawyers use them to sift through legal documents, while doctors rely on them for expert knowledge.