The context window is the maximum number of tokens an LLM can read and process in a single conversation turn, including system instructions, knowledge, and history.
A context window is the memory capacity of a large language model, defined as the total token limit it can simultaneously ingest across system prompts, retrieved knowledge chunks, and dialogue history.
Every word, number, and punctuation mark is converted into tokens (~4 characters per token). If a conversation and its retrieved context exceed the model’s window, text is truncated, leading to forgotten rules or lost context.
Efficient RAG pipelines optimize context window usage by retrieving only the top-3 to top-5 most relevant chunks and pruning conversational history.
SiteMind dynamically prunes and packs retrieved knowledge chunks to fit comfortably within Gemini’s context window, ensuring fast token generation without information loss.
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Related Technical Concepts
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique where an AI fetches verified passages from a knowledge base before answering, ensuring grounded, factual replies.
Semantic Chunking
Semantic chunking breaks long documents and web pages into focused, self-contained sections so retrieval systems can pull exact answers without token bloat.
Time-To-First-Token (TTFT)
Time-To-First-Token (TTFT) measures the latency between a user sending a message and the first visible word appearing in the chat window.
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