Senior Data Science Lead

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Large Language Models (LLMs)

Understanding Transformers (Part 2): How LSTMs fixed Recurrent Networks (mostly)
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 • Jose Parreño Garcia
Understanding Transformers (Part 1): Why RNNs are nearly impossible to train
A gentle walkthrough in how Recurrent Neural Networks work, and the math that breaks them.
LLMs explained (Part 6): Smarter AI through Reinforcement Learning
Why fine-tuning is not enough and how reinforcement learning with human feedback shapes smarter models.
 • Jose Parreño Garcia
LLMs explained (Part 5): Reducing hallucinations by using tools
LLMs still make things up. Can web search and code execution fix that?
 • Jose Parreño Garcia
LLMs explained (Part 4): Making LLMs actually useful through fine-tuning
How models go from glorified autocomplete to real-world assistants (and where they still fail).
 • Jose Parreño Garcia
LLMs explained (Part 3): From tokens to training – how a baseline LLM learns
How raw text becomes numbers, why tokenization matters, and what an LLM actually learns when it starts from scratch.
 • Jose Parreño Garcia
LLMs explained (Part 2): How LLMs collect and clean training data
You can't train a great AI on junk data. So how do companies collect, clean, and process the trillions of words that fuel modern LLMs?
 • Jose Parreño Garcia
LLMs explained (Part 1): The 3-layer framework behind chatGPT & friends
A practical breakdown of how language models generate text, learn patterns, and improve
 • Jose Parreño Garcia
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