Blog

#17: Red Teaming: Why Breaking LLMs Might Save Them

In earlier posts we looked at how LLMs are given direction through supervised fine-tuning / reinforcement learning from human feedback (RLHF) and how LLMs are kept safe. But just because a model can follow instructions doesn’t mean it should follow all of them.

6/22/2025

#16: Understanding How Safety is Built into LLMs

In my previous post, we explored how Large Language Models (LLMs) are trained to align with human preferences. Now, let's examine the broader landscape of LLM safety. As these models become increasingly integrated into our daily lives, ensuring their safe and ethical operation is critical.

6/2/2025

#15: Teaching LLMs to Align with Us: Supervised Fine-Tuning and RLHF

We’ve seen how large language models (LLMs) are pretrained on vast amounts of data and how they generate text one token at a time during inference.

5/25/2025

#14: How LLMs Generate Text: A Peek Inside the Inference Process

In the last few posts, we walked through how large language models (LLMs) are built — from collecting training data to breaking down words into tokens, and finally training the model on massive datasets. Today, we will discuss the inference process, which is the reason LLMs exist.

5/17/2025

#13: How LLMs Learn: A Beginner’s Guide to Training

In our last few posts, we explored what large language models (LLMs) are, how their data is collected and curated, and how that data is broken down into tokens. Now comes the question at the heart of it all: How do these models actually learn?

4/26/2025

#12 Understanding Tokenization in LLMs

In earlier posts, we explored how large language models (LLMs) work at a high level, followed by a deeper dive into how data is collected for training these models.

4/6/2025