Context Window Management
The strategic allocation and optimization of the finite amount of text an LLM can process in a single inference call.
Every LLM has a context window limit—the maximum amount of text it can "see" at once. For enterprise applications processing massive contracts or codebases, this is a critical bottleneck. BasaltHQ implements intelligent Context Window Management through hierarchical summarization, sliding window chunking, and priority-based context injection. When an agent in BASALTONYX reviews a 200-page legal document, it does not try to fit the entire document into one call. Instead, it creates a hierarchical summary tree, identifies the most relevant sections via semantic search, and injects only the critical clauses into the active context alongside the user query.
Related Concepts
See also:
LLM Reasoning Chain
A structured sequence of logical steps an LLM follows to arrive at a verifiable conclusion, analogous to showing your work in mathematics.
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Retrieval-Augmented Generation
A technique that grounds LLM responses in factual, enterprise-specific data by retrieving relevant documents before generating an answer.
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Prompt Engineering
The discipline of designing, testing, and optimizing the textual instructions given to an LLM to maximize the quality, accuracy, and consistency of its output.