Section 113 · Chapter 15, How Models Work
How Modern LLMs Are Trained and Tested
To test LLMs well, builders need a practical model of how they are made.
benchmarkmonitoringRLHFRLAIFtokenizationfine-tuningmodern llms trained tested
What to do
- Ask where the data came from, who had authority to include it, what licenses apply, what private data might be present, which languages and regions are missing, what time period the data represents, and which product risks are invisible in the corpus.
- Check whether benchmark questions leaked into training.
- Check whether important subgroups disappeared during "quality" filtering.
- Check whether synthetic data is labeled as synthetic.
- Check whether the holdout set is truly held out.
Evidence to preserve
- Preserve checkpoints and compare training loss with truly held-out performance throughout the run.
- Record model version, prompt version, tool versions, retrieval snapshot, memory state, route, latency, token cost, fallback behavior, and final output.
- Preserve the inputs, versions, configurations, raw outcomes, and results for benchmark, monitoring, RLHF, RLAIF needed to reproduce work on How Modern LLMs Are Trained and Tested.
- Report results for benchmark, monitoring, RLHF, RLAIF by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In a real release review, map observed failures to the model lifecycle. Ask whether the issue is caused by data, labels, tuning, retrieval, prompting, tools, decoding, safety policy, or product workflow. Useful LLM quality work often starts by naming the layer that can actually be changed.
Continue the conversation
Apply this to your context.
Save your product context once, then open a focused conversation that combines it with this concept.
Cite this page
Jason Arbon. "How Modern LLMs Are Trained and Tested." Testing AI Knowledge Edition, section 113.
https://jarbon.ai/testing-ai/knowledge/ch113-modern-llms-trained-tested.html