Natural Language Processing (NLP) Text Preprocessing & Generative Preparation for GenAI Assistant

We developed a script-based preprocessing pipeline tuned for the structure and content of the source corpus. The pipeline removes extraneous syntax including numbers, URLs, references, and structural artefacts like table-of-contents pages and recurring headers. It cleans narrative voice by detecting first-person constructions and rewriting them into the collective form used by the rest of the corpus. It standardises terminology against a canonical clinical vocabulary, eliminates unwanted named entities (brand names, proprietary product names), and applies a series of sentence-level normalisation rules tuned to the patterns observed in the original material. For every step where automated cleaning could not confidently resolve an inconsistency, the pipeline flags the affected passage and routes it into a manual proof-reading queue. The queue surfaces only the specific spans that need human attention rather than forcing reviewers to read entire documents, which dramatically reduced the human effort required while preserving the quality bar. The pipeline was built using a combination of NLTK for linguistic operations, named-entity and syntax filtering modules for domain-specific cleaning, and a structured manual QA layer that integrated with the team's existing review tools. Once cleaned, the corpus was passed into the generative-model input pipeline where it was tokenised and prepared for downstream training of GPT-2-class models via Hugging Face Transformers. The end result was a high-quality, governance-ready text corpus that the client could confidently use to train their generative model, with full visibility into what had been removed, what had been normalised, and which passages had been touched by human review.