Category
AI & Model Data
Training data, evaluation sets and testing before a model meets a customer.
These practical guides explain the decisions, data inputs, quality checks and operating controls teams need before they invest. Each article links the method to a relevant service, solution or free tool.
How to Evaluate an LLM Application Before Launch
Build an LLM evaluation plan covering representative cases, answer quality, safety, refusal, retrieval, human review and regression testing.
2 min read
AI Training Data Quality Framework
A practical framework for AI data collection, labeling, reviewer calibration, disagreement handling, validation, versioning and acceptance.
2 min read
Testing an AI Assistant Before It Talks to Customers
A pre-launch testing programme for AI assistants: real-user eval sets, refusal testing, red-teaming, drift baselines and the escalation path.
6 min read
How to Evaluate an AI Training Data Vendor: 12 Questions That Reveal Everything
How to evaluate an AI training data vendor with 12 direct questions, good and bad answers for each, and a table of annotation quality metrics to check.
6 min read
What “Model-Ready Data” Actually Means — And Why Most Datasets Aren't
Model-ready data is a measurable standard, not a synonym for clean. Four criteria, worked examples, and why volume alone won't fix a fine-tune.
7 min read
Next step
Discuss your use case
Bring one pain point, a data source, a workflow, a margin question. We'll come back with a focused assessment and a clear ROI hypothesis.