The GenAI Guidebook

The GenAI Guidebook#

GenAI capabilities used to be limited to researchers or folks in a few organizations. Not anymore. These capabilities are widely available, whether it be popular chat style interfaces, through apis, or open models. This is a guide for those looking to navigate this rapidly changing field.

You may ask Isn’t there already a lot of material out there? You’re right, and that’s precisely why this is a guide book. So much is being shared across papers Youtube, blog posts, tweets, GitHub etc, Keeping track of it all without feeling overwhelmed is a challenge in itself.

Here’s what’s inside

  1. A guided tour of the fundamentals

  2. The minimal resources needed to get a great understanding

  3. Deep Dives into particular libraries, topics and papers

Who’s this guidebook for?#

  • Applied Practitioners - Developers looking to utilize these models in some way and are want to understand how to shape them and include them in their use cases.

  • Curious Users - Folks being exposed to GenAI (which is everyone these days) who want to learn more

  • Thought Leaders - Those figuring out where we are so to figure out where to go next

See also

If you enjoy this you may also like my GenAI Book Club where every couple of weeks we spend an hour talking through a different topic.

Contents#

Here’s what to expect as you click into the various chapters

  • End to End Span - From the mathematical fundamentals to how GenAI fits in the real world

  • Intuitive Explanations - A distillation of each topic into a couple of paragraphs

  • Code Tutorials - Learn through hands on code tutorials

  • Production Grade Code - Become proficient with the same tools the professionals use

  • Curated References - A distillation of the best external resources for each topic

Read Non Linearly!#

If you know what you to read just skip to it.

For those unsure here’s the learning approach I suggest

  1. Start with fundamentals - Knowing the basic theory and how to read the code will make understanding everything else easier.

  2. Tune a model on your own - Tuning a model will give you a fantastic sense of how these work, don’t work. This chapter is not written yet but soon will be

  3. Read in whatever order you want - There are many branching off points here, from reading into particular training methods, to red teaming. Pick whatever suits your interests.

What this guide is not#

  • Self Contained Resource - This is a guide to navigate you to the best content. It is not meant to be encyclopedia

  • Academic article or textbook - We’re not presupposing that you have detailed level of knowledge of every topic or writing in the style of traditional academia.

  • A static body of work - The field is moving fast, this text will change accordingly.

Constant work in progress#

With so much happening in this field it’s challenging to keep up, especially with well written content.

For some articles you may see the following status at the top. Here’s what to expect if you do.

  1. References Only - Some bullet points and links to resources

  2. Draft Text - The start of the text article, though typos will be present

  3. Expansion in progress - Adding additional elements such as video or code

Who am I?#

My name is Ravin Kumar. I’ve found generative models to be fascinating for a long time. Here’s where you can find my other work.