# Desk log KB-BIND-REPLICA-20260822

**Status:** First-party sanitized composite; not a customer extract, independent benchmark, third-party dataset, certification, media report, or endorsement  
**Page:** https://infinisynapse.com/en/blog/bind-knowledge-base-to-database  
**Run ID:** `KB-BIND-REPLICA-20260822`  
**Date:** 2026-08-22  
**Evidence package checked:** 2026-08-31
**Operator:** InfiniSynapse Data Team  
**Attestor:** William Zhu, InfiniSynapse cofounder ([GitHub @allwefantasy](https://github.com/allwefantasy))  
**Contact for contradictions:** zhuhl@infinisynapse.com

## What this file is

A downloadable record of two replicas sharing column names. It documents the six-step method, input boundary, row-use counts, and artifact inventory. The companion [aggregate CSV](https://infinisynapse.com/blog-media/bind-knowledge-base-to-database/downloads/aggregate-KB-BIND-REPLICA-20260822.csv) contains only the published aggregate rows—not 16,400 or 11,200 raw rows.

## Verification materials

- [Verify the aggregate CSV](https://infinisynapse.com/blog-media/bind-knowledge-base-to-database/downloads/verify-KB-BIND-REPLICA-20260822.py)
- [External-source check](https://infinisynapse.com/blog-media/bind-knowledge-base-to-database/downloads/external-source-check-KB-BIND-REPLICA-20260831.md)
- [Independent-reproduction protocol](https://infinisynapse.com/blog-media/bind-knowledge-base-to-database/downloads/independent-reproduction-protocol-KB-BIND-REPLICA-20260831.md)

**Independent reproduction:** No qualifying unaffiliated report was known as of 2026-08-31. The verify script checks only published rows and does not reproduce either private sanitized source.

## Six-step method (reproducible)

1. Pick one source you are authorized to read. Prefer a replica or sanitized extract. Write its name down.
2. Write or export a small pack: ten field notes and one approved report.
3. Upload the pack, then bind it to that one source. Binding is a separate click from upload.
4. Ask one goal with that source selected—not a request for a SQL snippet.
5. Inspect the artifacts and confirm the retrieved passages belong to the bound pack.
6. Ask the same goal next week. A successful replay keeps the same definition.

## Source and goal

| Field | Value |
|---|---|
| Sources | Sanitized 16,400-row `orders_replica_2026` and 11,200-row `orders_archive_2025` |
| Source grain | One row per sanitized order record |
| Source version | Fixed desk extracts dated 2026-08-22 |
| Collision | Shared column names; 2026 fee notes vs 2025 archive rows |
| Pack metadata | First-party sanitized composite; owner: InfiniSynapse Data Team |
| Bind mapping | Finance pack ↔ `orders_replica_2026` |
| Retrieval configuration | Bound-pack retrieval; configuration values not published |
| Unbound baseline | Same goal with shared names and no bind |
| Goal asked twice | “What is last-month fee-excluded margin?” |

## Row-use counts

| Retrieval state | 2026 replica rows used | 2025 archive rows used |
|---|---|---|
| Unbound (shared names) | 16,400 | 11,200 |
| Bound to 2026 replica | 16,400 | 0 |

The extract did not change. The bind changed which schema was allowed to use those notes. Wall-clock for the bound rerun was 17 minutes (warehouse time excluded).

## Task artifacts

| Retrieval state | SQL | Memo | Chart | CSV | Correct-source passages |
|---|---|---|---|---|---|
| Unbound (shared names) | 1 | 0 | 0 | 0 | 0 |
| Bound to 2026 replica | 1 | 1 | 2 | 1 | 2 |

## Minimum disclosure for independent replication

Disclose data grain and row count, source identity and version, pack version, the exact bind mapping, query and retrieval artifacts, model and prompt versions, unbound and bound results, run time, failures, and any commercial conflict of interest. Publish confirming and conflicting results.

## What you may cite

- Grain, collision, 16,400 / 11,200 versus 16,400 / 0, artifact counts 1 / 1 / 2 / 1, passages 0 → 2, run ID, ~17 min wall-clock

## What you may not claim

- Customer uplift, named-logo case, independent benchmark, certification, raw dataset, official EEAT score, or external validation

## External context (not validation of this run)

- [UK Government Aqua Book](https://www.gov.uk/government/publications/the-aqua-book-guidance-on-producing-quality-analysis-for-government) (retrieved 2026-08-31)
- [U.S. GAO Assessing Data Reliability](https://www.gao.gov/products/gao-20-283g) (retrieved 2026-08-31)
- [ASA Ethical Guidelines for Statistical Practice](https://www.amstat.org/your-career/ethical-guidelines-for-statistical-practice) (retrieved 2026-08-31)
- [Stanford HAI AI Index](https://hai.stanford.edu/ai-index) (retrieved 2026-08-28)
- [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) (retrieved 2026-08-28)
- [NIST Privacy Framework](https://www.nist.gov/privacy-framework) (retrieved 2026-08-28)
- [OWASP Top 10 for LLM Applications](https://owasp.org/www-project-top-10-for-large-language-model-applications/) (retrieved 2026-08-28)
- [ISO/IEC 27001](https://www.iso.org/isoiec-27001-information-security.html) (retrieved 2026-08-28)
- [Microsoft Azure data architecture guide](https://learn.microsoft.com/en-us/azure/architecture/data-guide/) (retrieved 2026-08-28)
- [AWS Well-Architected Machine Learning Lens](https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/welcome.html) (retrieved 2026-08-28)