Play Before You Understand

Starting with practical experience rather than theoretical understanding can lead to better outcomes when using advanced machine learning tools. Newcomers may have an advantage over experienced researchers, as they approach these technologies without preconceived notions. The common practice of fine-tuning models often proves to be a misallocation of time and resources, as it may not yield the expected benefits. Many assume that customizing models will help integrate specific data, such as internal documentation, but this is often more complex and costly than anticipated. Ultimately, relying too heavily on fine-tuning can detract from effectively utilizing existing capabilities.

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Episode notes

The first episode of The Pragmatic Engineer Podcast is out. Expect similar episodes every other Wednesday. You can add the podcast in your favorite podcast player, and have future episodes downloaded automatically.

Listen now on Apple, Spotify, and YouTube.

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On the first episode of the Pragmatic Engineer Podcast, I am joined by Simon Willison.

Simon is one of the best-known software engineers experimenting with LLMs to boost his own productivity: he’s been doing this for more than three years, blogging about it in the open.

Simon is the creator of Datasette, an open-source tool for exploring and publishing data. He works full-time developing open-source tools for data journalism, centered on Datasette and SQLite. Previously, he was an engineering director at Eventbrite, joining through the acquisition of Lanyrd, a Y Combinator startup he co-founded in 2010. Simon is also a co-creator of the Django Web Framework. He has been blogging about web development since the early 2000s.

In today’s conversation, we dive deep into the realm of Gen AI and talk about the following:

• Simon’s initial experiments with LLMs and coding tools

• Why fine-tuning is generally a waste of time—and when it’s not

• RAG: an overview

• Interacting with GPTs voice mode

• Simon’s day-to-day LLM stack

• Common misconceptions about LLMs and ethical gray areas

• How Simon’s productivity has increased and his generally optimistic view on these tools

• Tips, tricks, and hacks for interacting with GenAI tools

• And more!

I hope you enjoy this episode.

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