“Gold”: the truth standard that allows AI to be rigorously controlled

The illusion of progress without a frame of reference

One of the common pitfalls in AI projects is believing that we are making progress... when the rules of evaluation have changed along the way. A new data set, a new taxonomy, a modified prompt: all these elements can give the impression that the system is better. But without a stable reference, the comparison no longer makes sense.

This is where the comes in Gold : one reference data set, carefully selected and fixed, which became the standard of measurement. It does not change with iterations. It constitutes a fixed point in the face of changing models, data and methods.

What Gold really brings

Gold is not limited to evaluating an isolated model. It allows you to measure the performance ofThe entire AI pipeline :

Make reliable the data (quality, consistency, absence of duplicates).
Complete or enrich the information (classification, reconciliation, extraction of attributes).
Analyze to produce forecasts or answer business questions.

In each of these cases, Gold provides a basis for calculating clear indicators : precision, reminder, F1-score, Recall @k for research, MAPE for forecasts... These metrics translate technical performance into concrete elements that businesses can understand and use to make decisions.

The principles of robust Gold

Designing a Gold is a demanding exercise that is based on a few fundamental rules:

Representativeness : the corpus must reflect operational reality, including rare but critical cases.
Stability : Gold is fixed during an entire evaluation phase. When the scope changes, we create a new version (v2), but we keep the history.
Annotation quality : the data is labelled with explicit rules and a consistency check between annotators.
Traceability : each Gold element must be documented (origin, date, rights, compliance).
Business weighting : not all mistakes are created equal. A false negative in fraud does not have the same cost as a product classification error.

These principles transform Gold into a management tool, not just a technical artifact.

From the laboratory to the field: offline and online coordination

Gold is the basis for so-called evaluations Offline : fast, replicable and inexpensive. They make it possible to compare several approaches with each other and to filter the best ones. But the real truth comes from the field, through Online tests (A/B testing, business indicators).

So the key is the link between the two:

Offline (Gold) : measure, learn quickly, detect signals.
Online (production) : confirm the real impact (time saved, customer satisfaction, reduction of errors).

Without Gold, the transition to production is based on beliefs. With Gold, it's based on facts.

Use case: an e-commerce distributor

A distributor wants to improve the management of its products: deduplication and classification.

Gold V1 : 10,000 pairs annotated “match/non-match”, 5,000 cards classified in a taxonomy.
First assessment : F1-score of 0.82 in deduplication, 86% accuracy in classification.
New iteration : collection of “difficult” examples, enrichment of the model with image + text → F1-score increases to 0.89, classification to 90%.

Result: transition to production validated with confidence, because progress is measured objectively.

Governance and measurement culture

Adopting Gold also means establishing a culture of AI governance :

A data quality manager ensures the consistency of the corpus.
Gold versions are archived and documented.
The indicators are monitored by segment, and linked to business KPIs.

It is an approach that brings technical teams and business departments together around a common language: measured performance.

Conclusion

In an environment where models and data are constantly evolving, the Gold Is it that stays stable. It provides a clear framework for evaluating, comparing, and deciding. For AI, data and business departments, it is not an academic luxury: it is the condition for transforming experimentation into measurable progress and for aligning AI with the strategic challenges of the company.