Build a benchmarking workbench that tests whether an AI system retrieves the correct information from a growing collection of stored notes, documents, and conversation summaries.
Allow developers to import a knowledge corpus, create questions with expected evidence, and configure retrieval strategies such as keyword search, metadata filters, and vector-search result imports.
Include:
A benchmark dataset editor with questions, expected answers, and supporting passages.
A retrieval trace showing which source fragments were returned.
Metrics for recall at configurable result counts, precision, and reciprocal rank where applicable.
A detector for missing evidence, contradictory passages, and irrelevant retrieval.
A comparison of retrieval configurations across benchmark versions.
A context-size estimator based on supplied token counts or a configured tokenizer.
A report linking answer failures to retrieval failures or insufficient source material.
Exportable benchmark results and regression tests.
Do not claim to measure a vector database or language model that has not actually been connected. Keep retrieved evidence distinct from generated answers, and avoid treating semantic similarity alone as proof of correctness. Include a fictional knowledge base and a reproducible evaluation suite.
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