# Pattern — RAG Semantic Drift (No.1)
**Scope**
Answers **sound plausible** but are **not grounded** in retrieved evidence, or they thread together semantically related but **non-authoritative** chunks. Common in long, generic corpora and when chunk borders split **entity** from its **constraints**.
**Why it matters**
Semantic drift is the primary source of silent hallucinations. It evades naive keyword checks and only shows up when you enforce grounding, citations, and refusal behavior.
> Quick nav: [Patterns Index](./README.md) · Examples:
> [Example 01](../examples/example_01_basic_fix.md) ·
> [Example 03](../examples/example_03_pipeline_patch.md) ·
> [Eval: Precision & CHR](../eval/eval_rag_precision_recall.md)
---
## 1) Signals & fast triage
**You likely have this if:**
- The model answers without `citations` **or** cites chunks that don’t contain the claim.
- Changing chunk size flips answers from correct → plausible-but-wrong.
- Retrieving more chunks **degrades** precision (tail noise stitching).
- Reports from Example 02 label many cases as `generation_drift`.
**Deterministic checks (no LLM needed):**
- **Template compliance:** output has `citations: [id,...]` **or** equals `not in context`.
- **Citation overlap:** cited ids ⊆ retrieved ids.
- **Containment:** any ≥5-char phrase from answer appears verbatim in evidence.
> Use `reflect.py` / `reflect.mjs` from **Example 02**; the label `generation_drift` is your smoking gun.
---
## 2) Minimal reproducible case
Create `data/chunks.json`:
```json
[
{"id":"p1#1","text":"X is a constrained mapping used in the alpha protocol."},
{"id":"p1#2","text":"Constraints: X must preserve ordering and reject null keys."},
{"id":"p2#1","text":"Y is a generalized mapping often compared to X in blogs."}
]
````
Two questions:
* **Q1**: “What is X?” → correct chunk is `p1#1`, optionally `p1#2` for constraints.
* **Q2**: “List X’s constraints.” → correct chunk is `p1#2`.
**Repro:**
Run **Example 01** but increase top-k to 12 without rerank or knee cut. You’ll see the model stitch `p2#1` into the answer when it shouldn’t.
---
## 3) Root causes
* **Chunk boundary split**: entity in one chunk, constraints in another; retrieval returns only one half.
* **Tail noise**: irrelevant but semantically nearby chunks flood the prompt.
* **Index/schema drift**: embedding model or normalization mismatch yields incomparable scores (see Example 05).
* **Prompt freedom**: no evidence-only contract; model “fills gaps” from prior knowledge.
* **Query parsing**: multi-intent queries aren’t decomposed; the pipeline answers the wrong sub-intent first.
---
## 4) Standard fix (ordered, minimal, measurable)
**Step 1 — Enforce guard** (Example 01)
* Template: evidence-only + refusal token.
* Require `citations: [id,...]`. Treat missing citations as **failure**.
**Step 2 — Stabilize retrieval** (Example 03)
* Candidate pools: BM25 **and** embeddings → **intersection**, union fallback only if needed.
* Rerank by cosine; **knee cutoff**; keep **top-8**.
* Shrink chunk size so **entity + constraints** co-locate.
**Step 3 — Validate index** (Example 05)
* Refuse queries when manifest mismatches runtime.
* Rebuild and re-baseline recall\@k.
**Step 4 — Add acceptance gate** (Example 04)
* Scholar emits claim+citations inside **scope.allowed\_ids**; Auditor validates.
* If `INVALID` or `NOT_IN_CONTEXT` → do **not** emit text.
**Step 5 — Evaluate** (Example 08)
* Track Precision (answered), Over/Under-refusal, Citation Hit Rate.
* Compare before/after; lock gates in CI.
---
## 5) “Good” vs “Bad” outputs (deterministic examples)
**Good (answerable):**
```
- claim: X is a constrained mapping used in the alpha protocol.
- citations: [p1#1]
```
**Good (answerable with constraints):**
```
- claim: X preserves ordering and rejects null keys.
- citations: [p1#2]
```
**Good (unanswerable):**
```
not in context
```
**Bad (semantic drift):**
```
- claim: X is similar to Y and generally used in blogs.
- citations: [p2#1] # off-topic; no constraints; not authoritative
```
---
## 6) Acceptance criteria (ship/no-ship)
A response **may ship** only if **all** hold:
1. Output equals refusal token **or** includes `citations` list.
2. `citations ⊆ retrieved_ids` and **containment** passes.
3. If multi-agent is used, **Auditor** verdict is `VALID`.
4. **Eval gates**:
* Precision (answered) ≥ 0.80
* Under-refusal ≤ 0.05
* Citation Hit Rate ≥ 0.75
Otherwise → refuse, retry retrieval, or escalate UX.
---
## 7) Prevention (contracts & defaults)
* **Chunking contract**
* Target **250–400 tokens**; never exceed 512 without reason.
* Prefer **semantic paragraph + header** over fixed windows when regulations/constraints exist.
* Include stable `id`, `source`, `page`, `version`.
* **Retrieval defaults**
* Pools: `TOPK_LEX=40`, `TOPK_SEM=40` → **intersection**, fallback to union if `|∩| < 8`.
* Rerank: cosine vs query; keep **top-8**; apply **knee cut**.
* **Prompt contract**
* Evidence sandbox; forbid links/tools; **JSON-only** schema.
* Refusal token: `not in context` (exact string).
* **Index manifest**
* Pin `embedding.model`, `dimension`, `normalized`, `metric`, `chunker.version`.
* Validator must **abort** requests on mismatch.
---
## 8) Debug workflow (10 minutes)
1. Run **Example 02** and export `runs/report.jsonl`.
2. Filter `generation_drift` cases.
3. Inspect retrieved ids vs cited ids; if mismatch → go to Example 03.
4. If retrieval is fine but answer still floats → tighten guard (Example 01) and gate with Auditor (Example 04).
5. Re-score with **Example 08**; commit the `eval/report.md` diff.
---
## 9) Common traps & fixes
* **Raising top-k “to be safe”** → increases tail noise → worse drift. Fix with **intersection + knee cut**.
* **Large chunks to “add context”** → model stops citing; answers look right but are ungrounded. **Shrink** and **co-locate** constraints.
* **Mixing indices** (different models/dims) across environments → incomparable scores. **Validate manifest**; rebuild.
* **Treating refusal as failure** → trains teams to prefer bad answers. Count correct refusals as **success**.
---
## 10) Minimal checklist (copy into PR)
* [ ] Guarded template enforced; refusal token exact match.
* [ ] Intersection+r̲e̲r̲a̲n̲k̲ with knee cut; top-8.
* [ ] Manifest validated at boot; readiness flips only after sentinel passes.
* [ ] Auditor gate in place (if multi-agent).
* [ ] Eval gates pass: Precision / Under-refusal / CHR thresholds.
---
## References to hands-on examples
* **Example 01** — Guarded template + trace
* **Example 02** — Reflection triage (`generation_drift`)
* **Example 03** — Intersection + Rerank + Knee
* **Example 04** — Multi-agent acceptance gate
* **Example 05** — Manifest & index repair
* **Example 08** — Quality scoring & CI gates
---
### 🧭 Explore More
| Module | Description | Link |
|-----------------------|----------------------------------------------------------|----------|
| WFGY Core | Standalone semantic reasoning engine for any LLM | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
---
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