Large Developer Desk Mat

How do large language models actually generate code?

LLMs generate code by predicting the most likely next token based on patterns learned from billions of lines of code during training. They don't "understand" code the way humans do — they recognize statistical relationships between tokens. When you provide a prompt, the model samples from a probability distribution to produce output that statistically resembles correct code for your context.

How does Cursor manage context across a large codebase?

Cursor uses a combination of: (1) auto-indexing your codebase into embeddings for semantic search, (2) explicit @file mentions to pin specific files, (3) .cursorignore to exclude irrelevant files, and (4) the active file plus recent files as implicit context. For large monorepos, using @mention liberally and keeping your context focused on the relevant subdirectory produces the best results.

What is Sourcegraph Cody and how does it handle large codebases?

Sourcegraph Cody is an AI coding assistant specifically designed for large, complex codebases using Sourcegraph's code intelligence platform for context. Unlike tools that limit context to open files, Cody can search across your entire codebase using Sourcegraph's code graph, making it particularly valuable for understanding large legacy systems or enterprise monorepos where the relevant code could be anywhere.