Articles
Vol. 4, No. 2 (2026)
University of Indonesia
10.58477/cj.v4i2.510
Published: 2026-08-30
Abstract
AI shopping assistants increasingly employ agent-based retrieval, combining lexical search, structured filtering, and LLM-mediated selection. However, effective retrieval often requires knowledge beyond textual matching. This exploratory single-case study of an Indonesian grocery e-commerce assistant triangulates 279 observations, 52 failure traces, 74 practitioner-reported defects, a schema audit, and four practitioner interviews. The analysis identifies six retrieval-critical knowledge dimensions and reveals that externalization fails at two distinct layers. At the product data-model layer, essential fields (allergens, dietary constraints, age suitability) were absent and persisted despite architectural changes. At the retrieval-schema layer, existing knowledge failed to reach candidates: structured filters appeared in only 29% of calls, and correctly invoked filters often returned empty sets from non-empty pools. Addressing these layer-specific failures, the study proposes a structured knowledge framework based on the knowledge management process cycle, positioning GraphRAG as a future direction.
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Vol. 4 No. 2 (2026)
How to Cite
How to Cite
Wahyudi, S. B., Sensuse, D. I., Lusa, S., Qurani, M. H., Yasin, Y., & Mailinda, I. (2026). Absent or Unretrievable: A Two-Layer Knowledge Management Framework for Retrieval-Critical Product Knowledge in a Grocery E-Commerce AI Shopping Assistant. Computer Journal, 4(2), 320-327. https://doi.org/10.58477/cj.v4i2.510
| DOI | https://doi.org/10.58477/cj.v4i2.510 |
| Publication Date | 2026-08-30 |
| Journal Section | Articles |
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