<ul data-eligibleForWebStory="false"><li>Large-language models (LLMs) have a context window that limits the information they can process, including prompts, documents, tool-call schemas, and ongoing conversations.</li><li>The context window of an LLM is measured in tokens, which are bite-sized chunks of text that the model can understand, not in letters or words.</li><li>If the context window overflows, the oldest tokens are silently dropped, known as Context Window Overflow (CWO).</li><li>Strategies like filtering before vector search, re-embedding on version changes, hashing before embedding, and respecting token limits are key aspects of context engineering for LLM models to maintain efficiency and accuracy.</li></ul>

Context Engineering. Intro & Pragmatic Take

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