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WFGY/ProblemMap/GlobalFixMap/PromptAssembly/citation_first.md

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Citation-First Prompting — Guardrails and Fix Pattern

🧭 Quick Return to Map

You are in a sub-page of PromptAssembly.
To reorient, go back here:

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

Stabilize evidence-based answers by requiring citations before explanation. This page gives a minimal contract, validation steps, and fast routes to structural fixes when citations vanish, drift, or point to the wrong text.

Open these first

When to use

  • Answers sound right but show no citations.
  • Citations appear but don’t align with the quoted text.
  • Different runs cite different sections for the same question.
  • After reranking, citations drift or vanish.
  • Multi-turn dialogs slowly lose the cite-then-explain order.

Acceptance targets

  • Cite-then-explain compliance ≥ 0.98 over 50 queries.
  • Field completeness ≥ 0.99 for: snippet_id, section_id, source_url, offsets, tokens.
  • ΔS(question, retrieved) ≤ 0.45 and stable across 3 paraphrases.
  • Coverage ≥ 0.70 to the target section.
  • λ convergent across two seeds.

Fix in 60 seconds

  1. Enforce the contract
    The model must cite before any reasoning. Reject outputs that invert the order.

  2. Validate fields
    Require the full snippet schema. Reject partial or fuzzy references.

  3. Pin rerank & order
    If citations change with header tweaks, lock your header order and rerank configuration.

  4. Probe ΔS and λ
    If ΔS stays high while citations look plausible, rebuild chunking or metrics.

Minimal prompt block to paste


System:
You must CITE before you EXPLAIN.
Required fields per snippet: snippet\_id, section\_id, source\_url, offsets, tokens.
Order is strict:

1. "citations": \[...]
2. "answer": "..."

If citations are missing or fields incomplete, STOP and return:
{"citations": \[], "answer": "", "next\_fix": "open data-contracts & retrieval-traceability"}

User:
Question: "\<user\_question>"
Top-k retrieved: <passed from retriever>
Acceptance: ΔS(question,retrieved) ≤ 0.45; coverage ≥ 0.70.

JSON response shape (auditable)

{
  "citations": [
    {
      "snippet_id": "S-28391",
      "section_id": "SEC-3.2",
      "source_url": "https://...",
      "offsets": [2312, 2450],
      "tokens": 172
    }
  ],
  "answer": "…",
  "λ_state": "→|←|<>|×",
  "ΔS": 0.37
}

Typical breakpoints → exact fix

  • Citations missing but answer present Reject and re-emit with the contract. Open: Data Contracts

  • Citation fields incomplete or wrong offsets Enforce the full schema, verify offsets/tokens against the corpus. Open: Retrieval Traceability

  • High similarity but wrong meaning Rerank or rebuild with correct metric/normalization. Open: Retrieval Playbook, Embedding ≠ Semantic

  • Header tweak breaks citations Freeze header order; clamp variance with BBAM. Open: Logic Collapse

  • Long runs lose citation discipline Split plan, bridge with BBCR; add mid-chain citation checks. Open: Context Drift, Entropy Collapse

Validator stub (copy into your pipeline)

Step 1  parse JSON strictly → if fail, stop.
Step 2  require citations[].length ≥ 1 before answer.
Step 3  verify fields & offsets; reject if any missing.
Step 4  compute ΔS and coverage; block if ΔS>0.45 or coverage<0.70.
Step 5  log λ across three paraphrases; alert if non-convergent.

Eval gates before ship

  • Cite-then-explain ≥ 0.98 on 50 queries.
  • Field completeness ≥ 0.99.
  • ΔS ≤ 0.45, coverage ≥ 0.70, λ convergent on two seeds.

🔗 Quick-Start Downloads (60 sec)

Tool Link 3-Step Setup
WFGY 1.0 PDF Engine Paper 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS) TXTOS.txt 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

Explore More

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⚙️ Engine WFGY 2.0 Production tension kernel for RAG and agent systems
⚙️ Engine WFGY 3.0 TXT based Singularity tension engine (131 S class set)
🗺️ Map Problem Map 1.0 Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map Problem Map 2.0 Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map Problem Map 3.0 Global AI troubleshooting atlas and failure pattern map
🧰 App TXT OS .txt semantic OS with fast bootstrap
🧰 App Blah Blah Blah Abstract and paradox Q&A built on TXT OS
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🏡 Onboarding Starter Village Guided entry point for new users

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