Learning Path: Large Language Models

LLM purpose and capabilities

Large Language Models (LLMs) are AI models—typically transformer-based—trained on vast text corpora to perform natural language processing tasks, especially language generation. Their core capability is predicting and producing text in context, enabling them to generate, summarize, translate, and analyze language. In practice, LLMs are often trained first as next-token predictors (e.g., GPT-style) and then adapted via fine-tuning (such as instruction tuning and methods like RLHF) to follow user instructions and behave more like assistants. LLMs’ capabilities extend beyond basic text generation through architectural and training choices (e.g., attention mechanisms and context windows, and sometimes mixture-of-experts for efficiency). They can also be extended using external tools and data sources via techniques such as retrieval-augmented generation, tool use, and prompting strategies (prompt engineering). Because LLMs can be sensitive to training data quality and can produce fluent but incorrect outputs (hallucinations), evaluation and safety measures—using benchmarks, perplexity-based metrics, and adversarial tests—are important for assessing reasoning, factual accuracy, alignment, and safety.

0/2 modes complete

Transformer architecture overview

0/2 modes complete