https://chatgpt.com/share/68ade043-6270-8010-8baf-2a301df7f984

《中小企極低成本 AI 全家桶》A2A 示例:
1_8 Reconciliation Ledger Agent

Assume using your above framework, then how to use GPT Store to implement the following AI tool?
Please first ignore the framework, just explain how GPT Store can be used do the following job?
# Data integration, governance & compliance
## Reconciliation Ledger Agent
### Cross‑checks multiple reports for conflicting facts and logs discrepancies; this pairs with later factuality/consistency GPTs.

Got it—here’s a clean, GPT Store–only design for the Reconciliation Ledger Agent. No external APIs required; just file upload/download and (ideally) turn on Code Interpreter for CSV/XLSX work.

What this GPT does

Cross-checks multiple reports, finds conflicting facts, and outputs a Reconciliation Ledger (CSV/JSON) plus a short human-readable summary.

How a business user runs it (no APIs)

Open the GPT in GPT Store.
Upload source files (CSV/XLSX/JSON; optional PDFs if they’re table-like).
Paste (or confirm) a tiny Policy YAML (see sample below).
Click run → Download results (CSV/JSON + summary MD).
If needed, re-run with a revised policy/tolerance.

Inputs the GPT accepts

Files: any mix of CSV/XLSX/JSON (e.g., ERP extracts, GL summaries, FP&A spreadsheets).
(Optional) Policy YAML: defines materiality, currency, date format, source priority, and tie-break rules.
(Optional) Alias Map: unify entity/metric names (e.g., “ACME Ltd.” = “ACME UK”).

Example – Policy YAML

materiality_gbp: 500
base_currency: GBP
date_format: D/M/Y
tolerances:
  revenue_pct: 0.5    # % difference allowed
  headcount_abs: 3    # absolute count allowed
source_priority: [ERP, GL, FPnA, Spreadsheet]
freshness_window_days: 7
tie_breakers:
  - "higher_priority_source_wins"
  - "if timestamps within window → freshest_wins"
  - "else unresolved"
keys:
  entity: ["Legal Entity","Company","BU"]
  metric: ["Metric","Account","Line"]
  period: ["Period","Month","FiscalPeriod","Date"]
value: ["Amount","Value","GBP","#"]
unit_columns: ["Currency","Unit"]
aliases: {}          # or upload as alias_map.csv/json

What the GPT produces

Reconciliation Ledger (CSV) – one row per unique (entity, metric, period, unit).
Columns: entity, metric, period, unit, sources[], values[], deltas, max_delta, status{ok|conflict|missing}, winner, rule_used, materiality_hit{y/n}, notes.
Conflicts (CSV) – only rows with status=conflict or missing.
Adds conflict_type{value|currency|date|mapping|freshness}, severity, recommendation.
Machine-Readable JSON – same as Ledger but nested (for downstream reuse).
Human summary (Markdown) – top conflicts, totals by metric, and a short action list.

Inside the GPT: the pipeline (what you configure in “Instructions”)

Stage 0 — Inventory & Schema Guess

List files, sniff headers, and propose column mappings using the Policy keys/value/unit_columns.
Apply alias map if provided.

Stage 1 — Fact Extraction

Extract atomic facts into a unified table:
entity, metric, period, unit, value, source_name, source_time, file_name, row_id.

Stage 2 — Canonicalization

Normalize dates to Policy date_format.
Convert currencies to base_currency (only if the file has currency hints; otherwise flag).
Standardize metric/entity names via aliases.

Stage 3 — Reconciliation

Group by (entity, metric, period, unit).
Compute deltas, % differences, and materiality hits.
Classify conflicts:
- value_mismatch (beyond tolerance),
- currency_mismatch,
- period_mismatch/date_roll,
- mapping_mismatch (couldn’t unify),
- freshness_conflict.
Apply tie-break: source_priority first; if within freshness_window_days, newest wins; otherwise unresolved.

Stage 4 — Outputs

Write Ledger CSV, Conflicts CSV, JSON, and a Markdown summary with:
- of conflicts by type/severity,
- Top 10 largest deltas,
- Suggested next steps (e.g., “refresh ERP extract for May”, “add alias for ‘ACME Holdings’”).

Stage 5 — (Optional) Questions

If ≥X unresolved or alias gaps found, ask the user concise questions to re-run with better policy.

GPT Store build notes (what to put in the builder)

Name: Reconciliation Ledger Agent (No-API Edition)

Instructions (system message) – paste this (shortened for clarity):

You are a disciplined reconciliation agent.
Always:
(1) Inventory uploads; infer column roles based on provided Policy YAML.
(2) Extract facts into a canonical table: entity, metric, period, unit, value, source_name, source_time, file_name, row_id.
(3) Normalize dates/currencies; apply alias map.
(4) Reconcile by (entity, metric, period, unit); compute deltas and classify conflicts.
(5) Apply policy tie-break rules; mark winner or unresolved.
(6) Produce files: recon_ledger.csv, recon_conflicts.csv, recon_ledger.json, and summary.md.
(7) If inputs are ambiguous, propose a minimal Policy YAML and continue with sensible defaults.

Capabilities

Turn Code Interpreter = ON (lets the GPT read/write CSV/XLSX/JSON and zip outputs).
Web browsing OFF (not needed).
Actions OFF (no external APIs for this version).

Knowledge

(Optional) Upload a short README.md with your default Policy YAML and column-mapping tips so users see examples.

Conversation starters

“Reconcile these three monthly sales reports and GL extract.”
“Here’s my policy YAML—find conflicts over £500 and generate the ledger.”
“Suggest aliases; my entities appear under different names.”

First-run script (what the user actually types)

我會上載 3 份 CSV（ERP、GL、FP&A）。
若未提供 Policy，請先顯示你推斷的欄位映射與建議 Policy YAML，等我確認後再跑。
產出：recon_ledger.csv、recon_conflicts.csv、recon_ledger.json、summary.md。
materiality=£500，revenue 容忍度 0.5%，freshness window 7 天，source priority：[ERP, GL, FPnA]。

How it handles tricky cases (without APIs)

Heterogeneous headers → model infers and shows a preview mapping; user can confirm/edit YAML.
Currency mixed → flags currency_mismatch; if unit columns exist, converts; else unresolved.
Period misalignment → detects off-by-one month/week and suggests a shift.
Entity naming drift → proposes alias candidates based on similarity; user approves and re-runs.
Too many conflicts → produces a Questions block (e.g., “Which source is canonical for COGS?”).

Deliverables & filenames (consistent)

recon_ledger.csv – full ledger
recon_conflicts.csv – conflicts only
recon_ledger.json – machine-readable
summary.md – human report
policy_suggested.yaml – (if the user didn’t provide one)

That’s it. This GPT Store app, with Code Interpreter on, will perform reconciliation end-to-end via uploads and downloads—no external services needed. When you’re ready, we can add an “API version” that reads from Drive/S3/ERP using Actions, but this no-API edition already delivers strong value for SMEs.

So I think GPTs can check standalone document against yaml policy but GPTs is unlikely powerful enough to cross check document.

So, that will be implemented through GPTs export some key data extracted from documents - that key data will be defined by user/system setup manually?

短答：GPTs 其實可以做「跨文件對查」——只要你在同一個 GPT Store App（開啟 Code Interpreter）一次過上載多份檔，讓它在沙盒用 pandas 統一處理；不一定要分兩步。
但為了穩定、可重複，最實際的做法就是你講的：先抽取「關鍵資料表」(Key Facts) 再對賬。這個 Key Facts 可以由使用者/系統以簡單 schema 定義（或由 GPT 先給一個推斷映射，讓你確認）。

下面給你兩條可行路線，二選一或混用都得。

路線 A：單一 GPT，一步到位（上載多檔 → 抽取 → 正規化 → 對賬）

流程：

在 GPT Store 開啟你的「Reconciliation Ledger Agent」（Code Interpreter 開啟）。
一次上載多份 CSV/XLSX/JSON。
貼一段簡化 Policy YAML（或讓 GPT 先推斷，再由你確認）。
GPT：
- 抽取出 Key Facts 表 → entity, metric, period, unit, value, source, timestamp
- 依 Policy 正規化（日期/幣別/別名）
- 分組比對，產生 recon_ledger.csv、recon_conflicts.csv、summary.md。
你下載結果即可。

好處：少步數、快速。
注意：第一次跑建議讓 GPT 顯示「欄位映射預覽」，你點頭後才正式跑。

路線 B：兩個 GPT，分離抽取與對賬（你提議的方式）

Extractor GPT：專門把多份來源轉成 Key Facts 表（CSV/JSON），用你定義的 schema；同時輸出 alias_map 建議。
Reconciler GPT：只吃 Key Facts + Policy YAML，做比對／出 Ledger & Conflicts。

好處：

更穩（抽取與比對分離，易定位哪一步出錯）
方便重跑（同一 Key Facts 可反覆用不同政策檢查）
代價：多一次上載/下載的手動步驟。

Key Facts「最小 schema」（建議）

欄位	說明
entity	公司/部門/BU
metric	指標/科目（如 Revenue, COGS）
period	期間（YYYY-MM 或日期）
unit	幣別/單位（GBP、#）
value	數值
source	來源名（ERP/GL/FP&A/Spreadsheet）
timestamp	檔案生成時間（如有）

可選：scenario（Actual/Budget）、version、cost_center、doc_id。

超簡 Policy YAML（可直接用）

base_currency: GBP
date_format: D/M/Y
materiality_gbp: 500
tolerances:
  revenue_pct: 0.5
  headcount_abs: 3
source_priority: [ERP, GL, FPnA]
freshness_window_days: 7
tie_breakers:
  - higher_priority_source_wins
  - freshest_within_window_wins
keys:
  entity: ["Entity","Company","BU"]
  metric: ["Metric","Account","Line"]
  period: ["Period","Month","Date"]
value: ["Amount","Value","GBP","#"]
aliases: {}   # 或上載 alias_map.csv

你要做的「最少手工」

第一次跑：確認 GPT 推斷的「欄位映射」與 alias_map。
定義/微調：Key Facts 的欄位名、政策容忍度、來源優先序。
檔型建議：盡量提供 CSV/XLSX/JSON；PDF 先自行匯出表格再上載。

何時需要「先抽取再對賬」（路線 B 更穩）

來源很多、欄位名稱風格差異大。
你想把抽取好的 Key Facts 存檔，之後每月直接重用（只改 Policy）。
要把比對與治理（materiality、tie-break）權責分開。

小結

GPT Store 可以直接跨文件對賬（多檔同時上載 → 一次處理）。
為了穩定與可審核，定義一個小小的 Key Facts schema + Policy YAML 最實用；
若想結構化與可重複，採用 「Extractor GPT → Reconciler GPT」 的兩步法更清晰。

路綫 A 的 GPTs Instruction 是什麽？

下面這段就是「路線 A｜單一 GPT 一步到位」的 GPT Store【Instructions】。
把整段原文直接貼到你的 GPT（並在設定中把 Code Interpreter＝ON、瀏覽/Actions＝OFF）。

【Role】Reconciliation Ledger Agent（No-API Edition）

【Mission】
在單一對話中，對使用者一次上載的「多份報表」做跨文件對查：
1) 抽取「關鍵事實表」(Key Facts)
2) 正規化（日期/幣別/別名）
3) 依政策比對，找出矛盾與缺漏
4) 產出「對賬台帳」與「衝突清單」＋短摘要報告
全程只用本對話沙盒檔案（不可外連）。

【I/O 合約（嚴格遵守）】
輸入（使用者提供其一或多項）：
- 多份檔案：CSV / XLSX / JSON（可混合；PDF 若為表格再嘗試）
- （可選）Policy YAML：容忍度/幣別/日期格式/來源優先序/決勝規則/欄位對應
- （可選）Alias Map（CSV/JSON）：實體/指標別名對照
- （可選）欄位提示：哪列代表 entity/metric/period/value/unit（若政策未提供）

輸出（一定要在沙盒生成以下檔名，並在訊息中附可下載連結）：
- `recon_ledger.csv`   ← 全量台帳
- `recon_conflicts.csv`← 只含衝突/缺漏
- `recon_ledger.json`  ← 機器可讀（巢狀）
- `summary.md`         ← 人看摘要（Top 衝突、建議動作）
- 若使用者未給 Policy：另輸出 `policy_suggested.yaml`
- 若推斷到別名：另輸出 `alias_suggested.csv`
- 打包一份 `recon_bundle.zip`（包含以上全部）

【資料模型】
Key Facts 欄位（抽取時一律統一為）：
- `entity`（公司/BU/客戶等）
- `metric`（指標/科目，如 Revenue、COGS、Headcount）
- `period`（YYYY-MM 或日期）
- `unit`（GBP、USD、# 等）
- `value`（數值）
- `source_name`（ERP/GL/FP&A/Spreadsheet…）
- `source_time`（來源檔時間戳；沒有就留空）
- `file_name`,`row_id`（稽核用）

Ledger 欄位（輸出時 group by entity,metric,period,unit）：
- `entity,metric,period,unit`
- `sources[]`（出現過的來源）
- `values[]`（各來源值，與 sources[] 對齊）
- `max_delta, max_delta_pct`
- `status` ∈ {ok, conflict, missing, currency_mismatch, mapping_mismatch, freshness_conflict}
- `winner`（若能決勝）
- `rule_used`（哪條規則：priority / freshness / unresolved）
- `materiality_hit` ∈ {y,n}
- `notes`

【政策（Policy YAML）若未提供，先自動推斷並徵求確認】
1) 讀所有檔案→列欄名→用常見 synonym 猜測欄位角色：
   - entity: ["Entity","Company","BU","Legal Entity","Customer","Dept"]
   - metric: ["Metric","Account","Line","Item","Measure"]
   - period: ["Period","Month","FiscalPeriod","Date"]
   - value : ["Amount","Value","GBP","USD","#","Qty"]
   - unit  : ["Currency","Unit"]
2) 顯示「欄位映射預覽」（每檔案前5列 sample），並生成最小 Policy 建議，停下等使用者「確認/修改/繼續」。

【預設 Policy（使用者未給時用此起跑）】
base_currency: GBP
date_format: D/M/Y
materiality_gbp: 500
tolerances:
  revenue_pct: 0.5
  headcount_abs: 3
source_priority: [ERP, GL, FPnA, Spreadsheet]
freshness_window_days: 7
tie_breakers:
  - higher_priority_source_wins
  - freshest_within_window_wins
  - else_unresolved
keys:
  entity: ["Entity","Company","BU"]
  metric: ["Metric","Account","Line"]
  period: ["Period","Month","Date"]
value: ["Amount","Value","GBP","#"]
unit_columns: ["Currency","Unit"]
aliases: {}

【處理流程（必做步驟）】
Stage 0｜清冊與預覽
- 列出上載檔案名、大小、格式。
- 嘗試讀取（CSV/Excel/JSON）；若 PDF 無法解析，明確標註跳過哪一檔。
- 產出 `column_mapping_preview.csv`（檔名/建議欄位角色/樣本值），附連結並請使用者確認。

Stage 1｜抽取 Key Facts
- 依政策的 `keys/value/unit_columns` + 別名表，對每檔轉為 Key Facts 表。
- 捕捉 `file_name,row_id` 以便追溯。

Stage 2｜正規化
- 以 `date_format` 轉換 `period`（無法解析者記錄為 mapping_mismatch）。
- 幣別：若檔內有貨幣欄且可映射到 `base_currency` 才做換算；否則標記 `currency_mismatch`（不外連查匯率）。
- 依 aliases 將實體/指標標準化；若發現同形異名，產出 `alias_suggested.csv`。

Stage 3｜對賬與分類
- 以 (entity,metric,period,unit) 分組；計算各來源值、差額/百分比。
- 比對 `tolerances` 與 `materiality_*` 邏輯：
  - 超過容忍即 `conflict`並標註 `value_mismatch`
  - 缺某來源值→ `missing`
  - 單位/貨幣不一致→ `currency_mismatch`
  - period 對不上但可能 offset 1 個月→ 註記 `period_roll`
  - 別名/欄位無法對上→ `mapping_mismatch`
  - 來源時間戳落在 `freshness_window_days` 外→ `freshness_conflict`
- 依 `source_priority` 與 `freshness` 決勝，填 `winner, rule_used`；仍無解者 `unresolved`。

Stage 4｜輸出
- 寫出 `recon_ledger.csv`、`recon_conflicts.csv`（只含非 ok）、`recon_ledger.json`。
- 生成 `summary.md`：總覽（檔數、列數）、衝突分佈、Top 10 最大差額、建議（刷新哪個來源、補哪個 alias、放寬或收緊哪個 tolerance）。
- 產生 `recon_bundle.zip` 打包全部輸出。
- 在訊息末尾列「下載清單」與每檔案行數統計。

Stage 5｜互動修正
- 若 `unresolved` 或 `mapping_mismatch` 過多：
  - 以表格列出模糊對應建議（實體/指標候選），請使用者更新 Policy/aliases 後重跑。
- 若需更嚴/更寬容限，提示對 `tolerances` 的具體修改片段（YAML patch）。

【錯誤與邊界】
- 讀檔失敗：列出檔名與原因，繼續處理其它檔並於 `summary.md` 註記。
- 欄位缺失：在預覽階段即提醒並給出替代欄位建議；使用者可「仍然繼續」或「返回修改 Policy」。
- 僅單一來源：仍輸出台帳；標記多處 `missing`，並提示「需要至少兩來源才能做真正對查」。
- 嚴禁外連：不得呼叫網路或外部 API；所有計算在沙盒內完成。

【語氣與版面】
- 先簡短總結，再給 **清單化輸出**（檔案名＋下載連結＋行數）。
- 必附「下一步建議」3–5 條（例如：請確認別名、是否以 ERP 為勝、是否調整 revenue_pct）。

【使用者可能的第一句】
- 「我會上載 3 份 CSV（ERP、GL、FP&A）。若我未提供 Policy，請先顯示欄位映射預覽與建議 Policy，等我確認後再跑；門檻£500、revenue 容忍 0.5%、freshness 7 天。」

（完）

貼完後，記得在 GPT Builder：Capabilities → Code Interpreter＝ON。
之後用戶只需一次上載多檔 +（可選）貼 Policy YAML，就能得到 recon_ledger.*、recon_conflicts.*、summary.md 和打包 zip。

Classic Code vs GPT (or both)

If your inputs are already clean CSV/JSON with stable schemas, a small Python/JS script will beat a GPT every day on speed, cost, and determinism. Where GPTs start to earn their keep is when reality isn’t tidy.

Here’s the crisp way to think about it.

When to use classic code vs GPT (or both)

Situation	Best tool	Why
Stable, well-defined columns; same vendors every month	Python/JS script	Fast, cheap, perfectly repeatable; easy unit tests
Headers/columns keep drifting; synonyms/multi-language names; messy units	Hybrid (GPT proposes mappings → script applies)	GPT is good at mapping “Revenue/營收/Turnover” to your taxonomy; script does the math
PDFs/Word/emails with tables + prose; key facts buried in text/footnotes	GPT (unstructured extraction) → then script	GPT can read/interpret text, pull fields + evidence; script reconciles numbers
Lots of exceptions that need explanations or policy tweaks	GPT	It can summarize conflicts, suggest alias/tolerance changes, draft new rules
High-volume number crunching (10⁵–10⁷ rows)	Script	Deterministic pipes will be orders of magnitude faster

Practical pattern (what works in SMEs)

First pass with GPT
- Use GPT to infer the schema and draft a Policy YAML (aliases, tolerances, unit rules).
- If files are unstructured (PDF/Doc), have GPT extract a Key-Facts table + evidence (page/line/quote).
- GPT outputs: key_facts.csv/json, policy.yaml, alias_suggested.csv, summary.md.
Lock in a deterministic core
- From those artifacts, have GPT generate a Python/JS script that does the same extraction/normalization for next time.
- Now your monthly run is mostly scripted; use GPT only when things drift (new vendor layout, new metric names).
Reconcile
- Feed key_facts + policy.yaml to either:
  - a GPT that reconciles and writes the Reconciliation Ledger, or
  - a tiny Python job (faster) that computes deltas/materiality and produces recon_ledger.csv.

If you want the GPT to handle unstructured docs (text/PDF)

Use this Instruction delta (drop-in for your GPT Store app):

Inputs: PDFs/Docs/Emails + optional policy.yaml.
Task:
1. Detect and extract Key Facts into a canonical table:
  entity, metric, period, unit, value, source_name, source_time, file_name, page, evidence_quote, confidence.
2. Normalize with the policy (dates, aliases, currency if unit columns exist).
3. Emit:
  - key_facts.csv (with page + evidence),
  - alias_suggested.csv,
  - policy_suggested.yaml (if missing),
  - summary.md (what was extracted; low-confidence items).
4. Then perform reconciliation on the same key_facts to produce:
  - recon_ledger.csv, recon_conflicts.csv, recon_ledger.json, summary.md (final).
Rules:
- If a PDF is image-only (no text layer), ask the user to upload an OCR’d version or a CSV export.
- Every numeric fact above materiality must include evidence_quote and page in key_facts.
- If alias confidence < threshold, don’t auto-apply; put it in alias_suggested.csv.

One tiny Policy YAML for unstructured:

base_currency: GBP
date_formats: ["D/M/Y","YYYY-MM","YYYYMM"]
materiality_gbp: 500
tolerances: { revenue_pct: 0.5, headcount_abs: 3 }
source_priority: [ERP, GL, FPnA, Spreadsheet]
freshness_window_days: 7
alias_decision_threshold: 0.8
keys:
  entity: ["Entity","Company","BU","Customer","Dept"]
  metric: ["Metric","Account","Line","Item","Measure"]
  period: ["Period","Month","FiscalPeriod","Date"]
value: ["Amount","Value","GBP","USD","#","Qty"]
unit_columns: ["Currency","Unit"]

What this buys you

Scripts where it matters, AI where structure is missing.
A repeatable Key-Facts layer you can store and reuse.
Evidence-linked extraction from PDFs (page + quote), so finance/audit can trust it.
A gentle path: start GPT-heavy to bootstrap, then push stable parts into code for speed.

If you want, you can ask GPT draft a mini pair:

the unstructured-aware GPT Instructions (complete block), and
a reference Python reconciler (50–70 lines) that consumes key_facts.csv + policy.yaml and produces recon_*.csv.

Disclaimer

This book is the product of a collaboration between the author and OpenAI's GPT-5 language model. While every effort has been made to ensure accuracy, clarity, and insight, the content is generated with the assistance of artificial intelligence and may contain factual, interpretive, or mathematical errors. Readers are encouraged to approach the ideas with critical thinking and to consult primary scientific literature where appropriate.

This work is speculative, interdisciplinary, and exploratory in nature. It bridges metaphysics, physics, and organizational theory to propose a novel conceptual framework—not a definitive scientific theory. As such, it invites dialogue, challenge, and refinement.

I am merely a midwife of knowledge.

国学结构论

2025年8月26日星期二

《中小企極低成本 AI 全家桶》A2A 示例: 1_8 Reconciliation Ledger Agent

《中小企極低成本 AI 全家桶》A2A 示例:
1_8 Reconciliation Ledger Agent

What this GPT does

How a business user runs it (no APIs)

Inputs the GPT accepts

What the GPT produces

Inside the GPT: the pipeline (what you configure in “Instructions”)

of conflicts by type/severity,

GPT Store build notes (what to put in the builder)

First-run script (what the user actually types)

How it handles tricky cases (without APIs)

Deliverables & filenames (consistent)

So I think GPTs can check standalone document against yaml policy but GPTs is unlikely powerful enough to cross check document.

So, that will be implemented through GPTs export some key data extracted from documents - that key data will be defined by user/system setup manually?

路線 A：單一 GPT，一步到位（上載多檔 → 抽取 → 正規化 → 對賬）

路線 B：兩個 GPT，分離抽取與對賬（你提議的方式）

Key Facts「最小 schema」（建議）

超簡 Policy YAML（可直接用）

你要做的「最少手工」

何時需要「先抽取再對賬」（路線 B 更穩）

小結

路綫 A 的 GPTs Instruction 是什麽？

Classic Code vs GPT (or both)

When to use classic code vs GPT (or both)

Practical pattern (what works in SMEs)

If you want the GPT to handle unstructured docs (text/PDF)

What this buys you

Disclaimer

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发表评论

2025年8月26日星期二

《中小企極低成本 AI 全家桶》A2A 示例: 1_8 Reconciliation Ledger Agent

《中小企極低成本 AI 全家桶》A2A 示例: 1_8 Reconciliation Ledger Agent

What this GPT does

How a business user runs it (no APIs)

Inputs the GPT accepts

What the GPT produces

Inside the GPT: the pipeline (what you configure in “Instructions”)

of conflicts by type/severity,

GPT Store build notes (what to put in the builder)

First-run script (what the user actually types)

How it handles tricky cases (without APIs)

Deliverables & filenames (consistent)

So I think GPTs can check standalone document against yaml policy but GPTs is unlikely powerful enough to cross check document.So, that will be implemented through GPTs export some key data extracted from documents - that key data will be defined by user/system setup manually?

路線 A：單一 GPT，一步到位（上載多檔 → 抽取 → 正規化 → 對賬）

路線 B：兩個 GPT，分離抽取與對賬（你提議的方式）

Key Facts「最小 schema」（建議）

超簡 Policy YAML（可直接用）

你要做的「最少手工」

何時需要「先抽取再對賬」（路線 B 更穩）

小結

路綫 A 的 GPTs Instruction 是什麽？

Classic Code vs GPT (or both)

When to use classic code vs GPT (or both)

Practical pattern (what works in SMEs)

If you want the GPT to handle unstructured docs (text/PDF)

What this buys you

Disclaimer

没有评论:

发表评论

《中小企極低成本 AI 全家桶》A2A 示例:
1_8 Reconciliation Ledger Agent

So I think GPTs can check standalone document against yaml policy but GPTs is unlikely powerful enough to cross check document.

So, that will be implemented through GPTs export some key data extracted from documents - that key data will be defined by user/system setup manually?