Post 1
Fine-Tuning an LLM: 6 Steps to Measure Improvement
Your model returns valid JSON in a demo, then wraps a production response in a paragraph of explanation. You add another instruction. The n…
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
Supervised Fine Tuning: Build a Reliable SFT Dataset
A fine-tuning run can complete without an error and still teach the model the wrong behavior. The cause can hide in the examples: overrepre…
Post 3
AI Data Labeling: A Practical Workflow for Better Quality
Quality checks need to cover both model suggestions and items the model may have missed.AI data labeling quality depends on clear rules, se…
Post 4
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…
Post 5
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 6
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…