Training a foundational GenAI model is like training your puppy. Understanding the process can enable you to have a helpful companion.
Chances are you hear about AI and “training” more times in a day than you care to admit. Since you have a curious mind, lets get into the details in a relatable way …
Data Collection:
First, we gather all the toys, treats, and training tools we could possibly need—like stocking up on every kind of chew toy, dog bed, and leash you can imagine. Every Wikipedia page, every news article, every blog, meme, and all the cat pics. You name it, it’s in there. We collect it all so the model, just like our puppy, has a solid foundation and doesn’t end up chewing on the wrong things.
Pre-training:
Now that we’ve got all the supplies, it’s time to start teaching the basics. Think of this as the unsupervised exploration phase. The puppy gets to wander around, sniff everything, figure out how things work—like learning which items are toys and which are off-limits, and why barking at 3 a.m. might not be the best idea. The model, similarly, learns the general structure and patterns from all the data we’ve given it.
Fine-tuning:
Here, we get specific about the tricks we want the puppy to learn. We say, “Hey, all that curiosity is great, but can you focus on this specific behavior?” Maybe we want it to sit, stay, or even do a cool trick like rolling over. It’s like helping our puppy develop specialized skills—except this one doesn’t need belly rubs for motivation (though maybe it should!).
RLHF (Reinforcement Learning with Human Feedback):
Then we bring in positive reinforcement—real humans who say, “Good job!” or “No, not like that!” It’s a bit like teaching a puppy with treats and gentle corrections—except here we’re shaping ethical responses, not just trying to keep the slippers intact. We’re making sure the model understands what’s acceptable and what isn’t.
System Prompt Design:
Next, we set the house rules. We tell the puppy, “You’re allowed to play, but don’t chew on the shoes.” It’s the equivalent of reminding our furry friend to stay within boundaries, be polite, and not jump on guests. We set the parameters so that the model, just like the puppy, behaves in a way that’s functional and safe.
Deployment and Monitoring:
Finally, we let our puppy out into the world, but we keep a careful eye on it. You know how you watch to make sure your pup doesn’t dig up the garden or eat something it shouldn’t? Same idea. We need to make sure the model isn’t doing anything… let’s say, unexpected. We monitor, tweak, and make sure it’s on its best behavior.
So, why care about all this? Because if you’re going to work with Gen AI, it’s super helpful to understand which stage you can influence. Unless your organization is building a model from scratch, you are likely only able to influence the behavior of the model through the system prompt and fine-tuning.
