Grok works by combining a large language model with retrieval and orchestration components that manage how information flows into and out of the model. At its core, Grok uses a transformer architecture that processes input text as tokens, encodes them into high-dimensional representations, and generates output tokens one by one. This mechanism allows Grok to understand grammar, intent, and context across long conversations. When you ask a question, the system first prepares the prompt, applies system-level instructions, and then feeds the prompt into the model for inference.
Beyond basic text generation, Grok is designed to incorporate external information sources. One important aspect is its ability to reference recent public data, especially content from the X platform. This is typically handled through a retrieval step that runs before or alongside generation. The system identifies relevant recent posts or signals, summarizes or filters them, and injects them into the model’s context window. This approach is similar in spirit to retrieval-augmented generation (RAG), where the language model is guided by external documents rather than relying purely on its internal parameters.
For developers, this architecture maps well to common AI system designs. If you are building your own Grok-like application, you might store embeddings of documents, logs, or messages in a vector database such as Milvus or a managed option like Zilliz Cloud. When a query arrives, you convert it into an embedding, retrieve the most relevant vectors, and pass the retrieved text to the model as additional context. Grok follows a similar conceptual flow, even if the exact infrastructure is abstracted away. Understanding this pipeline helps developers reason about latency, accuracy, and how to control what information the model can use.