Towards Data Science outlines essential topics for LLM engineers

8 articles · Updated · Towards Data Science · May 9

The article maps tokenisation, embeddings, transformer architectures, pre-training, fine-tuning, reinforcement learning, inference optimisation, evaluation and prompt engineering for designing, training and deploying real-world systems.
It highlights practical trade-offs including attention bottlenecks, hallucination reduction through retrieval-augmented generation, and efficiency methods such as LoRA, FlashAttention, KV caching, quantisation and speculative decoding.
The piece frames LLM engineering as a full-stack discipline spanning data pipelines, alignment, monitoring and behaviour drift, arguing reliable deployment depends on combining model design with evaluation and production safeguards.

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