Introduction to Large Language Models
A large language model learns statistical patterns in language and uses context to generate tokens. It can summarize, translate, extract fields and produce code, but its output still needs task-specific verification.
#What “large” means
The term refers to model scale and training, not a guarantee that the system knows everything on the internet. Training data, architecture and post-training differ. More parameters alone do not determine whether a model is suitable for your application.
Tokens are units used by the model, not necessarily whole words. The context window limits how much input and output a request can contain. Long context helps supply documents but does not guarantee every relevant detail will be used correctly.
#How applications adapt a model
Prompting supplies instructions and examples at request time. Retrieval supplies external documents. Fine-tuning changes model parameters using additional training examples. These are different mechanisms; uploading a document to a chat does not necessarily train the underlying model.
#A practical extraction task
For the fictional input “Alex uses Python and SQL,” a useful output contract is:
json{"name": "Alex", "skills": ["Python", "SQL"], "years_experience": null}
The null value matters: the text did not state years of experience. Evaluate whether the model preserves that uncertainty rather than rewarding a complete-looking record.
#Limitations to test
Models can hallucinate, inherit biases, miss context and produce inconsistent answers. Low sampling temperature does not prove correctness. Use fixed test cases, validate structured output and check important claims against authorized sources.
