NLP & Large Language Models

Getting machines to understand and generate human language — now dominated by large language models.

NLP & Large Language Models is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart LR
  T[/Text/] --> TOK[Tokenize] --> EMB[Embed] --> TR{{Transformer}} --> DEC[Decode] --> O[/Output/]
  RAG[(Your documents)] -. retrieve .-> TR

Key topics

• Tokenization & embeddings

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  Splitting text into tokens and mapping them to vectors that capture meaning.

• Language models

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  Models that predict text; scaling them produced the emergent capabilities of LLMs.

• Prompting

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  Steering an LLM with instructions, examples (few-shot), and chain-of-thought reasoning.

• Retrieval-augmented generation (RAG)

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  Ground an LLM in your own documents by retrieving relevant context at query time.

• Fine-tuning & alignment

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  Adapting base models with supervised fine-tuning and RLHF/DPO to be helpful and safe.

• Context, tokens & cost

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  Context windows, token limits, latency, and how pricing works in practice.

• Evaluation

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  Benchmarks, LLM-as-judge, and measuring hallucination, factuality, and task success.

Related areas

Deep Learning · Generative AI · AI Agents & Autonomy · Building with AI

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