The model was not the problem

Every retrieval, rerank and generation emits a span using the OpenTelemetry GenAI attribute names, so the question "was this the model or the retrieval?" is answered by reading the trace rather than by arguing about it.

Trace d04cfffd0ad64455

eval.improved · 1862 ms · status ok

Question: How often should the compressor oil filter be cleaned on the 125 CFM 100 PSI skid-mounted gasoline compressor?
Mode: improved
Documents returned
Answer refused
Cost: $0.00000

Span tree

retrieval retrieve

1862.3 ms

9 attributes

atlas.doc_ids ['tm-5-4310-221-15', 'tm-5-4310-221-15', 'tm-5-4310-221-15', 'tm-5-4310-221-15', 'tm-5-4310-221-15']

atlas.families ['C']

atlas.k 5

atlas.mode improved

atlas.n_results 5

atlas.pages [26, 192, 34, 29, 21]

atlas.reranked True

atlas.top_score 4.2564

atlas.withheld 0

The counterfactual: same model, correct passages

The argument that the bottleneck is retrieval is only worth making if it can be falsified. This runs the identical question through the identical model and the identical prompt, changing one thing: the second run is handed the passages the gold set says contain the answer. If the answer becomes correct, the model was never the constraint. The gold passages are the ones pipeline/questions.py located in the corpus, not passages chosen to make the point.

Index built by scripts/build_index.py at 2026-08-10 01:44:34 in 711s: 25 documents, 3,146 pages, 12,590 improved chunks / 5,434 naive chunks. Embeddings: Snowflake/snowflake-arctic-embed-s (int8 ONNX, Apache-2.0). Reranker: cross-encoder/ms-marco-MiniLM-L-6-v2 (ONNX, Apache-2.0).