Self-Healing Technical Documentation with Dynamic NLP
Keywords:
Self-Healing Documentation, Dynamic NLP, Transformer Models, Error Detection, Autonomous Mainte- nanceAbstract
The exponential growth of technical documen- tation in software and hardware ecosystems has necessitated autonomous systems capable of self-correction. This paper proposes a dynamic Natural Language Processing (NLP) framework for self-healing technical documentation, leveraging transformer models and adaptive feedback loops to detect and rectify errors in real time. We introduce a hybrid architecture combining rule-based heuristics and machine learning (ML) to address semantic inconsistencies, outdated content, and struc- tural ambiguities. Evaluations on a corpus of 10,000 technical documents demonstrate a 92% error correction rate, surpassing static NLP models by 28%. Latency benchmarks show sub- second response times for critical updates, with scalability up to 1 million documents. Our findings highlight the potential of dynamic NLP to reduce manual maintenance efforts by 65% while ensuring documentation integrity.
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