/3 min
OCR Confidence Scores: Using Them to Gate What Enters Your RAG Index
Use per-recognition OCR confidence as a metadata gate: drop, re-OCR, or flag low-confidence spans before they pollute your RAG index.
Topic
Data & Ingestion for AI
Document parsing, PDFs, and the pipelines that turn messy sources into clean retrievable context.
/3 min/reference
Layout-Aware vs VLM Parsing: When to Pay for a Vision Model to Read Your PDFs
Use VLM parsing for scanned, complex, or merged-cell PDFs; use layout parsers for clean digital docs to save cost and latency.
/4 min
Designing an Idempotent Document Ingestion Pipeline (Content Hashing, DLQs, Reprocessing)
Make a RAG ingestion pipeline idempotent by keying every stage on a SHA-256 content hash, isolating failures in DLQs, and reprocessing deterministically.
/4 min/reference
Cleaning Extracted Text Before Embedding: Headers, Footers, Hyphenation, and Noise
Strip repeating headers/footers, de-hyphenate line breaks, and normalize unicode before chunking so RAG embeddings stay clean.
/3 min
On-Device OCR for RAG: Apple Vision vs Tesseract for macOS Document Pipelines
On macOS, Apple Vision beats Tesseract for RAG ingestion — faster, more accurate on real scans, free, offline. Tesseract wins on Linux CI.