Baseline

The method for establishing a baseline is: select about fifty questions from real questions and write out the standard answers, run them through full-text search plus structured filtering and record the number of correct results, and that number is the baseline.

Teams with a baseline can rerun the question set when a next-generation model or index version is released to judge whether switching is worthwhile, while teams without a baseline have no way to compare.

Baruzzo's argument is not against vector search itself, but about the order of doing things: first establish a measurable baseline, and let error types drive technical decisions.

Without a baseline, adding vector search can only be called a "change", not an "improvement"; any newly added technical solution must be able to beat the baseline to count as an improvement.

An evaluation set is a collection of questions with standard answers or expected hit document annotations, recall measures the proportion the system actually returns out of all relevant documents that should be retrieved, and a baseline is the score of the simplest solution on the same evaluation set.

The method for establishing a baseline is to write down the expected hit answers for about fifty real questions, then run them with full-text retrieval plus structured filtering, and record the number of questions returned correctly as the baseline.

Any retrieval solution added afterward must be able to exceed the baseline number, otherwise it cannot be called an improvement, only a change.

The 7-day window was chosen because it covers a complete weekday/weekend cycle, can absorb intraday traffic fluctuations and periodic business peaks, and avoids misjudging a normal increase as an anomaly.

In the observability field, baselines are typically characterized by statistics over a sliding time window (such as 7 days or 30 days), including the median, P95/P99 percentiles, and variance.

The UP host argues that when writing a paper, one should first find a working baseline model codebase before considering improvements and optimizations.

In AI-collaborative development, satisfactory versions should be tagged and saved as baselines, adopting a workflow of freeze first, then experiment, with the ability to roll back — consistent with the logic of controlling variables in scientific experiments.

A baseline is essentially the reference state prior to an intervention; without a baseline, any claim of an X% improvement lacks credibility.

Starting data recording on the very first day of scaling up AI tools is the fundamental prerequisite for avoiding a situation where value cannot be clearly demonstrated later.

A rough but complete baseline model (such as logistic regression or a random forest with default parameters) should be established as quickly as possible to verify that the data pipeline is bug-free and to provide a reference point for improvement.