@Krishna70284154: A single project Training an LLM from scratch did two things for me: - It set me apart from most other candidates - It …
Summary
A tweet shares how training an LLM from scratch helped the author stand out in AI interviews and answer questions on Transformers, fine-tuning, and RAG confidently.
View Cached Full Text
Cached at: 07/11/26, 09:29 PM
A single project Training an LLM from scratch
did two things for me:
- It set me apart from most other candidates
- It let me answer almost every Transformer, LLM, and fine-tuning question with confidence
- Also made me answer RAG based questions more precisely
Most AI/ML interviewers are asking a lot of questions on LLM’s reading or watching videos doesn’t help much Implementing and getting your hands dirty will make you worthy
either follow @karpathy nanochat or @rasbt build a LLM or CS336 for this
Similar Articles
@akshay_pachaar: Andrej Karpathy summarized the entire history of LLM training in three nouns: - text - conversations - and environments…
Andrej Karpathy frames LLM training as text, conversations, and environments; Prime Intellect's Verifiers is an open-source framework for building and sharing RL environments for LLMs, released under MIT license, with a hub of 2500+ environments.
@_rohit_tiwari_: This 230-page book unlocks the secrets of LLMs. https://drive.google.com/file/d/1ZqV0wByb65_wvzWUbaLw6pCbtXgyXDHG/view……
A 230-page book that comprehensively covers LLM concepts including pre-training, fine-tuning, alignment, and prompting techniques.
@MLWhiz: I've spent the last two-plus months writing the GenAI Fundamentals series — my attempt at building the "how do LLMs act…
A comprehensive blog series by Rahul (MLWhiz) builds an understanding of LLMs from scratch, covering transformers, pretraining, and post-training in five parts.
@neural_avb: If you think about it, LLM training in 2026 is really a 3-step loop : - train it on some data - dogfood it/run categori…
The tweet outlines a 3-step loop for LLM training in 2026: train on data, run evals, and add synthetic data for underperforming tasks. It emphasizes the accessibility of legal distillation via open source models and cheap APIs, noting that training on reasoning traces alone can achieve high scores.
@RohOnChain: just talked to an MIT CS grad who is building the next frontier LLM. he told me this lecture by an OpenAI researcher on…
A tweet recommends a lecture by an OpenAI researcher on how LLMs are built, claiming it taught an MIT CS grad more than his entire degree.