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                "text": "Module 2: Chunk Atomicity and Inference Cost"
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        "markdown": "---\ntitle: Chunk Atomicity Inference Cost | NRLC.ai\ndescription: Chunk Atomicity Inference Cost on NRLC.ai. AI SEO services, structured data engineering, and LLM optimization solutions. Neural Command, LLC builds AI retrieval and...\norganization: Neural Command LLC\ncanonical: https://nrlc.ai/en-us/docs/prechunking-seo/course/chunk-atomicity-inference-cost/\n---\n\n← Back to Course Overview | ← Previous: Module 1\n\nWhy multi-fact chunks fail retrieval.\n\nThis module explains the data engineering principle: each chunk is a row, and each row should answer one question. Related: Crouton Specification\n\nEach chunk is a row.\n\nEach row should answer one question.\n\nThis is not content writing. This is schema design for retrieval systems.\n\nIf an LLM must:\n\n- resolve pronouns\n- infer scope\n- merge facts\n- assume context\nIt increases:\n\n- CPU cost\n- hallucination risk\n- citation avoidance\nLLMs avoid citing content that requires inference. They prefer explicit, self-contained facts.\n\nOne chunk = one assertion = one retrieval target.\n\nIf a chunk contains multiple facts, split it. If a chunk requires context, make the context explicit within the chunk.\n\nTask: Take a narrative paragraph from any existing page. Extract exactly five atomic facts (croutons) from it. Each fact must be a single declarative sentence.\n\nConstraint: No fact may contain pronouns, conjunctions, or implied context. Each fact must explicitly name all entities and relationships.\n\nWhat success looks like: You produce five sentences where each sentence can be read in isolation without ambiguity. If a sentence requires the previous sentence to be understood, it's not atomic.\n\nThis task is optional. No submission required. No validation. Use it to convert theory into applied thinking.\n\nNext: Module 3: Vectorization and Semantic Collisions\n\n\n\n---\n\nSource: https://nrlc.ai/en-us/docs/prechunking-seo/course/chunk-atomicity-inference-cost/\nPublisher: Neural Command LLC\nLicense: Editorial use with attribution\n"
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