Practical, careful explanations for preparing smaller, clearer, and safer inputs before using an AI model.
7 min read
Use uncertainty fields, source evidence, and validation checks to prevent invented details in AI document extraction.
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Remove names, project names, contract numbers, and confidential context while preserving the clauses an AI task needs.
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A practical workflow for measuring prompts, removing repeated material, and preserving the context an AI model actually needs.
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Understand token estimates, input and output limits, and why character counts do not map perfectly to AI usage.
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Convert documents into clean, reviewable text while preserving headings, lists, links, and essential meaning.
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Choose chunk sizes and boundaries that keep related ideas together and improve retrieval quality.
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Compare structured-data formats for prompt size, clarity, reliability, and safe round trips.
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A cautious workflow for identifying, replacing, reviewing, and restoring sensitive information.
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Clean source-code excerpts without damaging strings, syntax, examples, or information the model needs.
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Remove page furniture while preserving useful headings, lists, tables, links, and meaning.
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Estimate prompt size, reserve space for the answer, and avoid context-limit surprises before using an AI model.
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Turn a PDF into reviewable Markdown while preserving the headings, lists, tables, and reading order a RAG pipeline needs.
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Reduce JSON prompt size without changing values, breaking syntax, or removing the field names an AI task needs.
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