Corpus & Dataset Generation
I, among many others, find generic, off-the-shelf datasets to be noisy and irrelevant for specialized domains like networking and DoD compliance. That’s when I figured, why not build my own? A reliable AI stack starts with data engineered for the domain, and I’ve developed repeatable methods to generate corpora that are both clean and contextually aligned.
My Approach
Custom Scraping Framework – Built in Python with YAML-driven configs, headless browser support, rate limiting, and diff-based updates to capture structured data from Cisco docs, PDFs, KBs, and APIs.
Semantic Filtering – Lightweight embedding-based screening to automatically exclude irrelevant material, ensuring only in-domain knowledge makes it downstream.
Deduplication & Preprocessing – Hash-based duplicate detection, canonicalization, language normalization, and chunking strategies (token-boundaries, heading-aware, semantic splits).
Data Lineage & Versioning – Every dataset release is output with manifests, content hashes, and timestamps for reproducibility and compliance.
Advancing Further
I’ve already operationalized this pipeline for large technical corpora, but the framework is extensible. Next iterations include:
Adaptive Filters – Hierarchical classifiers to refine datasets dynamically as new domains are added.
Automated Metadata Enrichment – Injection of tags, section titles, and bidirectional source linking for improved retrieval.
Incremental Corpus Updates – Fiff-only syncs to keep datasets fresh without full rebuilds.
Why It Matters
The quality of embeddings, RAG pipelines, and downstream automation is directly dependent on dataset integrity. By controlling corpus generation at this level, I can reduce hallucination, increase semantic accuracy, and ensure models are trained and retrieved against data that reflects the real operational environment.
