Developer builds structured AI paraphrasing pipeline to prevent meaning drift

A developer building a tool called Paraphraser AI identified a core challenge with large language models: they produce fluent text but can silently alter facts, drop qualifiers, or change numbers during rewriting. To address this, the pipeline treats each rewrite request as structured data with separate fields for mode, strength, protected keywords, and output count, rather than a single open-ended prompt. Each rewrite mode and strength level is translated into a concrete instruction to improve model consistency and prevent vague UI labels from producing unpredictable results. Input validation rules are enforced before any model call, limiting text length, capping protected keywords, and requiring SEO mode to specify at least one keyword. The author acknowledges the pipeline reduces common failure modes but stops short of claiming it can fully guarantee semantic faithfulness in all cases.