Beginner's Guide to Generative AI

As everyone is starting to use AI more, it is essential that we all upskill ourselves in the field of Generative AI. It can be overwhelming to sift through all the links, videos, and shared articles. As a seasoned software developer and team leader, I am well-versed in foundational software engineering practices. To grasp the concepts in research papers, videos, and articles, I’ve found that applying systems design and coding as a learning approach has significantly accelerated my understanding. In case you're also in the same boat, here are the steps I followed and the notes I took on this method:

Basics on LLM:

https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/llm-foundations/

Prompt Engineering?

https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

What is RAG?

https://learn.deeplearning.ai/building-applications-vector-databases/lesson/3/retrieval-augmented-generation-(rag)

University research papers summary on RAG:

Stanford CS25: V3 I Retrieval Augmented Language Models (youtube.com)

Fine tuning:

https://learn.deeplearning.ai/finetuning-large-language-models/lesson/1/introduction

LLM Tooling:

https://learn.deeplearning.ai/langchain/lesson/1/introduction

Follow my Substack at:

https://open.substack.com/pub/chandrasdoodle/p/beginners-guide-to-generative-ai?r=3bjf5j&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true