Data Processing for AI
Prepare documents, records, and datasets for training, retrieval, and evaluation in Python.
Separate pipeline stages
Organize loading, decoding, cleaning, normalization, splitting, enrichment, validation, and export as explicit stages. Small functions make it easier to inspect intermediate results and rerun only the failed work. Pass records with stable identifiers and metadata rather than anonymous strings. Clear stages also help compare two preprocessing versions without confusing data errors with model behavior.
Normalize carefully
Normalization may include encoding repair, whitespace cleanup, date formats, language detection, duplicate removal, and document structure preservation. Do not erase information merely because it looks unusual; punctuation, headings, coordinates, and table relationships may matter for retrieval or evaluation. Keep the original source and transformation version so a questionable output can be traced back.
records = [{"id": "doc-1", "text": "Quarterly results", "source": "report.pdf"}]
for record in records:
if not record["text"].strip():
continue
chunk = {**record, "pipeline_version": "2026-07-18", "chunk_index": 0}
print(chunk)Preserve provenance
Carry document IDs, source locations, timestamps, labels, permissions, and processing versions through every transformation. Provenance supports citations, debugging, access control, deletion requests, and dataset audits. Store relationships between chunks and their parent document. An embedding without its source and permission metadata may be impossible to use safely in a multi-user application.
Quality and validation
Check required fields, encoding, size, language, duplicate rates, label balance, malformed records, and unexpected distributions before generating embeddings or training artifacts. Sample outputs for human inspection, especially after OCR or splitting changes. Fail loudly for systemic corruption and quarantine individual bad records with a reason. Quality checks should run again when data is updated.
Scale and resource limits
Stream large files instead of loading everything into memory, use bounded concurrency, checkpoint completed work, and write outputs atomically. Respect provider rate limits and local disk capacity. Make stages idempotent so a restart does not duplicate chunks, embeddings, or labels. Measure throughput, memory, retries, and failure counts to identify whether the bottleneck is I/O, CPU, network, or model inference.
Privacy and retention
Remove unnecessary personal data before processing and apply access filters before creating searchable artifacts. Review whether embeddings, summaries, and derived labels can still reveal sensitive information. Define retention and deletion across raw data, temporary files, indexes, caches, and exports. Protect local work directories and logs because preprocessing often handles more source detail than the final application displays.
Reproducible exports
Write a manifest containing source versions, filters, transformations, chunking settings, model versions, counts, checksums, and errors. Keep training, retrieval, and evaluation datasets distinct to reduce leakage. Compare distributions before and after a change, then run a fixed quality sample. A data pipeline is production-ready when it can be paused, audited, rerun, and deleted safely.