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 next response follows the schema but chooses
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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: overrepresented or conflicting examples, a prompt formatted
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, separate checks for different errors, and corrections that
Post 4
Data Labeling Services: Where Outsourced Labeling Breaks
Buy a labeling workflow you can inspectData labeling services turn raw examples into annotations: categories for text, boxes around objects, transcripts for audio, or ratings of model responses. Before comparing
Post 5
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 descriptions, or error logs. SQL can group identical text and apply
Post 6
Data Mining with Python: A Practical Approach to Extracting Insights
Key Takeaways- Modern data mining with Python uses an Arrow-native ecosystem where Pandas 2.2+, DuckDB, and Polars can reduce memory pressure for suitable workloads on a single machine.- Pandas 2.2+
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