Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs (Rough Cuts)

The advancement of Large Language Models (LLMs) has revolutionized the field of Natural Language Processing in recent years. Models like BERT, T5, and ChatGPT have demonstrated unprecedented performance on a wide range of NLP tasks, from text classification to machine translation. Despite their impressive performance, the use of LLMs remains challenging for many practitioners. The sheer size of these models, combined with the lack of understanding of their inner workings, has made it difficult for practitioners to effectively use and optimize these models for their specific needs.

This practical guide to the use of LLMs in NLP provides an overview of the key concepts and techniques used in LLMs and explains how these models work and how they can be used for various NLP tasks. The book also covers advanced topics, such as fine-tuning, alignment, and information retrieval while providing practical tips and tricks for training and optimizing LLMs for specific NLP tasks.

This work addresses a wide range of topics in the field of Large Language Models, including the basics of LLMs, launching an application with proprietary models, fine-tuning GPT3 with custom examples, prompt engineering, building a recommendation engine, combining Transformers, and deploying custom LLMs to the cloud. It offers an in-depth look at the various concepts, techniques, and tools used in the field of Large Language Models.

Topics covered

Coding with Large Language Models (LLMs)

Overview of using proprietary models

OpenAI, Embeddings, GPT3, and ChatGPT

Vector databases and building a neural/semantic information retrieval system

Fine-tuning GPT3 with custom examples

Prompt engineering with GPT3 and ChatGPT

Advanced prompt engineering techniques

Building a recommendation engine

Combining Transformers

Deploying custom LLMs to the cloud