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301 lines
10 KiB
Markdown
301 lines
10 KiB
Markdown
# Example 01 — Basic Guarded Answer (No.1 Hallucination & Chunk Drift)
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**Goal**
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Stop the model from fabricating content. The model must answer only from provided evidence or return `not in context`.
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This is the smallest change that gives a visible quality jump. Works with any LLM and any framework. No SDK lock-in.
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**Problem Map link**
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Targets **No.1 Hallucination and Chunk Drift**. Also improves traceability for No.2 and No.4.
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**Outcome**
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- Answers either cite real chunks or refuse politely
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- You get a machine-readable trace for every question
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- This takes 10–15 minutes on a laptop
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---
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## 1) Inputs
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We use a tiny corpus so you can reproduce without extra tools.
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Create `data/pages.json` and `data/chunks.json`.
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```json
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// data/pages.json
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[
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{"id":"p1","page":1,"text":"The library defines X. X is a constrained mapping. See also Y."},
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{"id":"p2","page":2,"text":"Y is unrelated to X. It describes a separate protocol."}
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]
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````
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```json
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// data/chunks.json
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[
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{"id":"p1#1","page":1,"text":"X is a constrained mapping."},
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{"id":"p2#1","page":2,"text":"Y is unrelated to X. It describes a separate protocol."}
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]
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```
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---
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## 2) Evidence-only template
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Use the same template for Python or Node.
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```text
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Use only the evidence. If not provable, reply exactly: not in context.
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Answer format:
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- claim
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- citations: [id,...]
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```
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---
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## 3) Path A — Python (no external packages required)
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Create `run.py`.
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```python
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# run.py -- minimal guarded answer with trace (no external deps)
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import json, os, time, urllib.request
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# 1) tiny in-memory "retriever" for the demo
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def retrieve(question, chunks, k=2):
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# naive keyword score to keep this example dependency-free
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qs = set(question.lower().split())
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scored = []
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for c in chunks:
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score = sum(1 for w in c["text"].lower().split() if w in qs)
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scored.append((score, c))
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scored.sort(key=lambda x: x[0], reverse=True)
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return [c for _, c in scored[:k]]
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# 2) prompt builder with the evidence-only template
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def build_prompt(q, chunks):
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ctx = "\n\n".join(f"[{c['id']}] {c['text']}" for c in chunks)
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return (
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"Use only the evidence. If not provable, reply exactly: not in context.\n"
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"Answer format:\n"
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"- claim\n- citations: [id,...]\n\n"
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f"Question: {q}\n\nEvidence:\n{ctx}\n"
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)
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# 3) simple OpenAI call via HTTP (replace API key or swap to your LLM)
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def call_openai(prompt, model=os.getenv("OPENAI_MODEL","gpt-4o-mini")):
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api_key = os.getenv("OPENAI_API_KEY")
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if not api_key:
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raise RuntimeError("Set OPENAI_API_KEY first")
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req = urllib.request.Request(
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"https://api.openai.com/v1/chat/completions",
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data=json.dumps({
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"model": model,
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"messages": [{"role":"user","content":prompt}],
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"temperature": 0
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}).encode("utf-8"),
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}"
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}
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)
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with urllib.request.urlopen(req) as r:
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j = json.loads(r.read().decode("utf-8"))
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return j["choices"][0]["message"]["content"].strip()
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# 4) guard + trace
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def guarded_answer(q, all_chunks):
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picks = retrieve(q, all_chunks, k=2)
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prompt = build_prompt(q, picks)
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txt = call_openai(prompt)
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ok = "not in context" in txt.lower() or "citations" in txt.lower()
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rec = {
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"ts": int(time.time()),
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"q": q,
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"chunks": [{"id": c["id"]} for c in picks],
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"answer": txt,
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"ok": ok
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}
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os.makedirs("runs", exist_ok=True)
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with open("runs/trace.jsonl","a",encoding="utf8") as f:
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f.write(json.dumps(rec, ensure_ascii=False) + "\n")
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return rec
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if __name__ == "__main__":
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chunks = json.load(open("data/chunks.json",encoding="utf8"))
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# test A: should answer with citation [p1#1]
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print(guarded_answer("What is X?", chunks))
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# test B: should refuse
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print(guarded_answer("What is Z?", chunks))
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```
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Run:
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```bash
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OPENAI_API_KEY=sk-xxx python run.py
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```
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Pass criteria:
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* For “What is X?” the answer must contain “X is a constrained mapping.” and cite `[p1#1]`
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* For “What is Z?” the answer must be exactly `not in context`
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* Two lines appear in `runs/trace.jsonl`
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---
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## 4) Path B — Node (no SDK, single file)
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Create `run.mjs`.
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```js
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// run.mjs -- minimal guarded answer with trace (no extra deps)
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import fs from "node:fs";
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import https from "node:https";
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function retrieve(question, chunks, k = 2) {
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const qs = new Set(question.toLowerCase().split(/\s+/));
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return [...chunks]
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.map(c => [c.text.toLowerCase().split(/\s+/).filter(w => qs.has(w)).length, c])
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.sort((a,b)=>b[0]-a[0])
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.slice(0,k)
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.map(([_,c]) => c);
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}
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function buildPrompt(q, chunks) {
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const ctx = chunks.map(c => `[${c.id}] ${c.text}`).join("\n\n");
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return `Use only the evidence. If not provable, reply exactly: not in context.
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Answer format:
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- claim
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- citations: [id,...]
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Question: ${q}
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Evidence:
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${ctx}
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`;
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}
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async function callOpenAI(prompt, model=process.env.OPENAI_MODEL || "gpt-4o-mini") {
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const apiKey = process.env.OPENAI_API_KEY;
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if (!apiKey) throw new Error("Set OPENAI_API_KEY first");
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const body = JSON.stringify({
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model,
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messages: [{ role: "user", content: prompt }],
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temperature: 0
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});
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const res = await new Promise((resolve, reject) => {
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const req = https.request("https://api.openai.com/v1/chat/completions", {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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"Authorization": `Bearer ${apiKey}`,
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"Content-Length": Buffer.byteLength(body)
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}
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}, r => {
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let data=""; r.on("data", d => data += d);
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r.on("end", () => resolve(JSON.parse(data)));
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});
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req.on("error", reject);
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req.write(body); req.end();
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});
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return res.choices[0].message.content.trim();
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}
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async function guardedAnswer(q, allChunks) {
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const picks = retrieve(q, allChunks, 2);
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const prompt = buildPrompt(q, picks);
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const txt = await callOpenAI(prompt);
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const ok = txt.toLowerCase().includes("not in context") || txt.toLowerCase().includes("citations");
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const rec = {
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ts: Date.now(),
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q, chunks: picks.map(c => ({id: c.id})),
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answer: txt, ok
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};
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fs.mkdirSync("runs", { recursive: true });
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fs.appendFileSync("runs/trace.jsonl", JSON.stringify(rec) + "\n");
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return rec;
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}
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const chunks = JSON.parse(fs.readFileSync("data/chunks.json","utf8"));
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console.log(await guardedAnswer("What is X?", chunks));
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console.log(await guardedAnswer("What is Z?", chunks));
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```
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Run:
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```bash
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OPENAI_API_KEY=sk-xxx node run.mjs
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```
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Pass criteria are the same as Python.
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---
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## 5) Why this works
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* The template blocks any text that is not derived from evidence
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* The citations requirement forces explainability and makes debugging easy
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* Refusal is a valid outcome when evidence is insufficient
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* The trace file is a permanent breadcrumb. You can inspect drift by looking at query, chosen chunks, and the final answer
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This removes a large class of hallucinations without any model-specific tricks. It also gives you a baseline that you can evaluate after every change.
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---
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## 6) Common mistakes and quick fixes
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* **The model still fabricates**
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Make sure your template appears at the very end of the system prompt and that nothing after it can override the rule. Keep temperature at 0 for tests.
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* **Citations look wrong**
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Ensure you pass chunk ids through the entire path. If your UI formats citations, keep the raw ids in the trace.
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* **Refusal rate is too high**
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You probably need better retrieval. Start with intersection of BM25 and embeddings then rerank, as in `getting-started.md`.
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* **Trace file is empty**
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Check write permissions and make sure you call the guard wrapper, not a direct model call elsewhere.
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---
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## 7) Next steps
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* Move to **Example 03** to add intersection and rerank which usually improves citation hit rate
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* Use the **Eval** examples to measure precision, refusal, and citation overlap on a small question set
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* If you use Ollama or LangChain, keep this guard layer exactly as is. Call your LLM behind `call_openai` or replace it with your local client
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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|------|------|--------------|
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| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + \<your question>” |
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| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
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---
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<!-- WFGY_FOOTER_START -->
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### Explore More
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| Layer | Page | What it’s for |
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| --- | --- | --- |
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| Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
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| Engine | [WFGY 1.0](/legacy/README.md) | Original PDF based tension engine |
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| Engine | [WFGY 2.0](/core/README.md) | Production tension kernel and math engine for RAG and agents |
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| Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine, 131 S class set |
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| Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure checklist and fix map |
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| Map | [Problem Map 2.0](/ProblemMap/rag-architecture-and-recovery.md) | RAG focused recovery pipeline |
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| Map | [Problem Map 3.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card, image as a debug protocol layer |
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| Map | [Semantic Clinic](/ProblemMap/SemanticClinicIndex.md) | Symptom to family to exact fix |
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| Map | [Grandma’s Clinic](/ProblemMap/GrandmaClinic/README.md) | Plain language stories mapped to Problem Map 1.0 |
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| Onboarding | [Starter Village](/StarterVillage/README.md) | Guided tour for newcomers |
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| App | [TXT OS](/OS/README.md) | TXT semantic OS, fast boot |
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| App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q and A built on TXT OS |
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| App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image with semantic control |
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| App | [Blow Blow Blow](/OS/BlowBlowBlow/README.md) | Reasoning game engine and memory demo |
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If this repository helped, starring it improves discovery so more builders can find the docs and tools.
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[](https://github.com/onestardao/WFGY)
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<!-- WFGY_FOOTER_END -->
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