What is an LLM?
A large language model is a neural network trained on extensive text and code to predict and generate sequences of tokens. Tokens are pieces of words or characters. By learning which tokens tend to follow others in many contexts, an LLM develops useful representations of language, concepts and patterns.
Transformers and attention
Most modern LLMs use the transformer architecture. Its attention mechanism helps the model weigh relationships among tokens in a context, even when relevant words are far apart. Training first builds a general model; instruction tuning and preference-based techniques then make responses more useful and safer.
What can LLMs do?
LLMs can summarize, classify, translate, extract information, answer questions, draft content and generate code. Developers can use prompting, structured outputs, tool calling, retrieval and fine-tuning to adapt them. The best approach depends on accuracy needs, privacy, latency and cost.
Limits and responsible use
An LLM generates plausible text; it does not automatically verify every claim. It can hallucinate, inherit bias, expose sensitive information or follow malicious instructions. Production systems need evaluation, access controls, source grounding, monitoring and human review for consequential tasks.
Learning to build with LLMs
Start with APIs and prompt design, then learn embeddings, vector search, RAG, tool calling and evaluation. Build an application that solves a defined problem and measure answer quality, groundedness, speed and cost rather than judging only a few impressive responses.
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