Post 1
Classify 100,000 Text Rows in SQL: Jev Benchmarks and a Practical Pipeline
Analytics and data engineering teams often face a table with an unstructured text column: customer feedback, support complaints, product de…
Data Processing
Learn how to transform raw, messy datasets into clean, model-ready inputs. This category covers ETL pipelines, data transformation strategies, normalization, deduplication, and structuring data for machine learning. Essential reading for anyone managing the data layer of an AI product.
Post 2
PII redaction for RAG: 5 checks before embedding
A support document contains a customer's account number. Your assistant hides it in the final answer, but the retrieval system still stores…
Post 3
PDF table extraction for RAG: 5 practical checks
A financial report puts revenue beside three year headings. Extraction keeps the numbers but loses their column labels. Retrieval finds the…
Post 4
LLM fine-tuning dataset quality: 6 checks before training
A fine-tuned model scores well on the test set. Then someone finds that several test questions are rewrites of training examples, with the…
Post 5
Automated Data Collection Tools in 2026: What Actually Works
You set up a scraper. Runs fine for 45 minutes. Then: CAPTCHA. Then 403. Then your IP is blocked and the proxy pool you paid for isn't answ…
Post 6
AI-Powered Data Pipeline Automation: A Practical Guide for 2026
Your data pipeline breaks at 3am. Again. The cron job that was supposed to transform customer events crashed silently, and now your analyti…