What's AI Podcast by Louis-François Bouchard

Fable is making our work worse.
Or have we just collectively accepted that better models need less review?
I have been noticing this everywhere: people who delegate technical work to AI (especially AI engineers) seem to produce worse outputs on everything else. Even something like the descriptions in a GitHub repo. The code is fine, great even. It’s everything around it that is slipping.
The results are bland and general. No personality, no life.
On the other hand, the less “technical” people I work closely with have improved in both quality AND productivity.
I don’t yet know exactly why, but here’s my attempt to put it all together…
P.s. This podcast episode is an experiment. It was entirely generated cloning my own voice on ElevenLabs... can you believe it? It is insanely good...

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Have you ever wanted to take a language model and make it answer the way you want without needing a mountain of data?
Well, OpenAI’s got something for us: Reinforcement Fine-Tuning, or RFT, and it changes how we customize AI models. Instead of retraining it with feeding examples of what we want and hoping it learns in the classical way, we actually teach it by rewarding correct answers and penalizing wrong ones, just like training a dog — but, you know, with fewer treats and more math.
Let’s break down reinforcement fine-tuning compared to supervised fine-tuning!
Both essentially have their use that we can discuss in one line:
Supervised fine-tuning teaches new things the model does not know yet, like a new language, which is powerful for small and less “intelligent” models.

While reinforcement fine-tuning orients the current model to what we really want it to say. It basically “aligns” the model to our needs, but we need an already powerful model. This is why reasoning models are a perfect fit.

I’ve already covered fine-tuning on the channel if you are interested in that. Today, let’s get into how RFT actually works!

ChatGPT is completely changing how we learn programming.

Instead of getting bogged down by coding theory, even beginners can jump right into building projects from day one.

Quite the difference compared to university!

With tools as simple as ChatGPT, you can experiment with building real applications right from the start quite easily without understanding much.

This hands-on approach lets you learn by doing, offering instant feedback and a way to explore coding in a practical, exciting way.

But there's a good and a wrong way to approach this.

Relying solely on copy-pasting code won’t make you a programmer.

When ChatGPT gives you a code snippet—say, a script that processes data or handles user login—use it as a starting point.

TAKE THE TIME to UNDERSTAND why the code works, experiment with modifications, and see how changes affect the outcome.

True mastery comes from engaging with the code, troubleshooting errors, and making it your own.

If you can't explain anything, even if your app runs, it won't make you a better programmer or get you a good job. It will also have the downside of making a precarious app. You'll one day end up with too much code to follow what's happening, and ChatGPT will be stuck in an endless debugging loop.

Yes, do embrace the power of AI to kickstart your projects, but just keep in mind that real growth (and value) happens when you do things and learn the logic behind every line.

We've built a whole course about that principle to learn Python: https://academy.towardsai.net/courses/python-for-genai?ref=1f9b29

Here's an overview of the impact of LLMs on human work, which is complex and varied across different job categories...