Research
Papers and techniques, explained plainly
Retrieval-augmented generation (RAG), from first principles
RAG is often explained as a stack of tools. Strip that away and it is one simple idea: let the model read the right material before it answers. Here is how it really works.
Why context length is hard to scale
A longer context window sounds like a simple knob to turn. Underneath it fights a cost that grows faster than the text — and attention that spreads thin.
Catastrophic forgetting and continual learning
Teach a neural network something new and it tends to forget what it knew. This stubborn problem is why models learn in big batches, not in a stream.
Chain-of-thought: why reasoning steps help
Asking a model to "think step by step" makes it noticeably better at hard problems. That is strange if you think about it. Here is why it works.
What RLHF actually does
RLHF is the step that turns a raw text predictor into something you can talk to. Here is what it actually changes — and, just as importantly, what it does not.
Distillation: teaching small models from big ones
Knowledge distillation trains a small model to imitate a large one. The trick is not copying answers, but copying the way the big model is unsure.
Evaluation beyond benchmarks: human and model judges
Benchmarks measure what is easy to score. For open-ended work you need judgment — from people, or from a model standing in for them. Both can mislead.
How models are evaluated: benchmarks, and why they lie
Benchmark scores look like measurements, but they are arguments. Here is how model evaluation actually works, and why a high number can still mislead you.
Tokenizers and why they matter for languages
A language model never sees words. It sees tokens. How text gets chopped into tokens quietly decides cost, speed, and fairness across languages.
Attention, in plain language
Attention sounds technical, but the idea is something you do every time you read. Here is what it really means inside a language model, without the math.
Hallucination, explained without the panic
A language model that makes things up is not malfunctioning — it is doing exactly what it was built to do. Here is why hallucination happens and how to manage it.
Synthetic data: training models on model output
When real data runs short, models can generate their own training data. It is powerful, slightly circular, and dangerous if you forget where it came from.
Fine-tuning vs RAG vs prompting: a decision guide
Three ways to make a model do what you want — and most teams reach for the heaviest one first. Here is how to choose in the right order.
Scaling laws: bigger, but why
"Make it bigger" sounds like a slogan, not a science. Scaling laws are what turned it into one. Here is what they actually say, and what they do not.
The transformer architecture, explained without math
The transformer is usually drawn as a wall of equations. Strip that away and it is one elegant idea: let every word decide which other words matter.
Pretraining vs fine-tuning vs alignment
Three words get blurred together when people describe how models are made. They are different stages with different jobs. Here is what each one does.
Emergent abilities: real or mirage?
Big models seem to suddenly "get" skills smaller ones lack. Is that a real phase change, or a trick of how we measure? The honest answer is: both.