An LLM application combines a language model with product logic, data, tools, prompts, and user experience to solve a specific workflow. Rather than treating the model as a standalone product, designers wrap it with surrounding code and content that turn raw generation capability into reliable, useful behavior. The composition typically includes prompts, retrieved documents, tool integrations, validation, and a user interface that all interact to deliver value.
Retrieval-augmented generation, commonly called RAG, is one of the most common patterns in LLM applications. RAG retrieves relevant external documents and adds them to the model context so answers can use fresh or private knowledge that the model has not seen during training. This is closely tied to grounding, which connects the model's output to provided sources, records, or tool results, reducing hallucinations and making answers easier to verify.
Other foundational concerns include prompt injection, which is an attack where untrusted content tries to override instructions, reveal secrets, or force unintended actions, and tool calling, which lets an LLM request structured actions such as database queries, web searches, calculations, or file edits. Designers also rely on evaluation sets, collections of representative prompts, expected behavior, and scoring rules used to test the app, and context window management, which decides what history, documents, tool results, and instructions fit into the model's limited input space.