LLMs Deep Dive
For engineers preparing for LLM and AI-engineering interviews who want to understand how large language models actually work, not just use them. You will be able to explain tokens, embeddings, attention, the training stages from pretraining to RLHF, decoding, fine-tuning and deployment trade-offs with worked numbers and real product examples.
Course Content
Prepares you to explain what an LLM is, how it is trained in stages, how it differs from older language models, and what "foundation model" means.
Prepares you to explain how text becomes tokens and vectors, why token counts drive cost and context limits, and why modern models have no truly unknown words.
Prepares you to compare encoder, decoder and encoder–decoder models, explain the objectives they are pretrained on, and say which one fits a given task.
Prepares you to walk through self-attention step by step with real numbers, and to explain positional encodings, multi-head attention and the KV cache that make transformers work in practice.
Prepares you to explain how an LLM turns next-token probabilities into text — greedy, beam search, temperature, top-k, top-p — and which setting fits which task.
Prepares you to explain the maths that trains an LLM — cross-entropy, gradients through embeddings, Jacobians, vanishing gradients, eigenvalues and KL divergence — with small numbers you can work on a whiteboard.
Prepares you to explain LoRA, QLoRA and distillation with parameter and memory numbers, and to say how you would stop a fine-tuned model from forgetting what it already knew.
Prepares you to spot and prevent overfitting when fine-tuning LLMs, and to explain the difference between generative and discriminative models and systems with examples.
Prepares you to discuss how current frontier models are built — native multimodality, mixture-of-experts and scaling trade-offs — while being honest about what labs have and have not published.
Prepares you to answer practical questions about context windows, hyperparameters, handling bad outputs in production, and the main risks of shipping LLM features, with the mitigation for each.