Large Language Models at Work RTM
Keeping with tradition, I'm writing the RTM post for Large Language Models at Work. The book is done. Now available on Kindle.
I decided not to contact a publisher this time around, for a couple of reasons: First, I didn't want the pressure of a contract and timelines (though looking back, I did finish this book faster than the previous two); Second, I had no idea if I will be able to write something that is still valuable by the time the book is done, considering the speed of innovation. More on this later.
I authored the book in the open, at https://vladris.com/llm-book/ and self-published on Kindle. Maybe I will look into making it a print book at some point, for now I'm keeping it digital.
Amazon offers a nice set of tools to import and format ebooks, but they have some big limitations - for example, no support for formatting tables, footnotes etc. I also couldn't convince the tool the code samples should be monospace on import so I had to manually re-set the font on each. The book has a few formatting glitches because of these limitations, which make me reluctant to look into a print book as I expect I will need to do a lot more manual tweaking for the text to look good in print.
Speed of innovation
I mused about this in chapter 10: Closing Thoughts. I'll repeat it here as it perfectly highlight why it is impossible to pin down this strange new world of AI.
I started writing the book in April 2023. When I picked up the project, GPT-4 was in private preview, with GPT-3.5 being the most powerful globally available model offered by OpenAI. Since then, GPT-4 opened to the public.
In June, OpenAI announced Functions - fortunately, this happened just before I
started working on chapter 6, Interacting with External Systems. Before
Functions, the way to get a large language model to connect with native code was
through few-shot learning in the prompt, covered in the Non-native functions
section. Originally, I was planning to focus exclusively on this implementation.
Of course, built-in support makes it easier to specify available functions and
the model interaction is likely to work better - since the model has been
specifically trained to
understand function definitions and output correct
In August, OpenAI announced fine-tuning support for
gpt-3.5-turbo. When I was
writing the first draft of chapter 4, Learning and Tuning, the only models that
used to support fine-tuning were the older GPT-3 generation models: Ada,
Babbage, Currie, and Davinci. This was particularly annoying, as the quality of
output produced by these models is way below
gpt-3.5-turbo levels. Now, with
the newer models having fine-tuning support, I had to rewrite the Fine-tuning
text-davinci-003 launched in November of 2022, while
on March 1st 2023. When I started writing the book,
backing most large language model-based solutions across the industry, and
migrations to the newer
gpt-3.5-turbo were underway.
deprecated to be removed by January 4, 2024 (to be replaced by
gpt-3.5-turbo-instruct), and the industry is moving to adopt GPT-4. I had to
update several code samples from
No idea how long the code samples will keep working or when OpenAI will decide
gpt-3.5-turbo or introduce an even more powerful model with
capabilities not covered in the book.
While some of the code examples will not age well as new models and APIs get release, the underlying principles of working with large language models that I walked through in this book - prompt engineering, memory, interacting with external systems, planning, and so on - will be relevant for a while. Understanding these fundamentals should help anyone ramp up in the space.
This is an exciting new field, that is going to see a lot more innovation in the near future. But I expect some of these fundamentals to carry on, in one shape or another. I hope the topics discussed in this book to remain interesting for long after the specific models used in the examples become obsolete.
Like with my previous books, I've been publishing excerpts as shorter, stand-alone reads. This might sound a bit strange in this case, as the book is already all online. But I figured it will hopefully help reach more people, and I did some work on each excerpt to remove references to other parts of the book so they can, indeed, be read wihtout context. I published all of these on Medium:
- N-shot Learning
- Embeddings and Vector Databases
- Interacting with External Systems
- Adversarial LLM Attacks
I hope you enjoy the book! Check it out here: Large Language Models at Work.