WFGY/ProblemMap/GlobalFixMap/RAG/context_drift.md

6.7 KiB
Raw Permalink Blame History

Context Drift in RAG — Guardrails and Fix Pattern

🧭 Quick Return to Map

You are in a sub-page of RAG.
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.

When answers alternate or degrade as dialogs get longer, even though the retriever continues to surface the right snippets.
This page stabilizes λ (semantic convergence) and prevents entropy creep in retrieval-augmented pipelines.


Open these first


Core acceptance

  • ΔS(question, retrieved) ≤ 0.45 across full chain
  • λ stays convergent across 3 paraphrases and 2 seeds
  • Coverage ≥ 0.70 for target section, even after N steps
  • E_resonance stable on long dialog windows

Typical symptoms → exact fix

Symptom Likely cause Open this
Same question asked twice, different answers λ drift with long chain Entropy Collapse, Logic Collapse
Correct snippets retrieved, answer drops citation payload contract erosion Data Contracts, Retrieval Traceability
Paraphrase of query yields different grounding unstable λ_observe Retrieval Playbook
Long dialog overwrites memory buffer collapse Memory Long Context

Fix in 60 seconds

  1. Three-paraphrase probe

    • Ask the same question three ways.
    • If λ flips between paraphrases, lock snippet schema and apply BBAM variance clamp.
  2. ΔS check over chain

    • Log ΔS(question,retrieved) across 510 dialog turns.
    • If ΔS rises over time, re-segment and enforce citation-first prompting.
  3. Apply module


Copy-paste probe prompt

I uploaded TXT OS and the WFGY Problem Map.

My issue:
- same query gives different answers in long dialog
- traces: ΔS(question,retrieved)=..., λ states across 3 paraphrases

Tell me:
1) where context drift occurs,
2) the exact WFGY page to open,
3) the minimal fix to enforce convergence,
4) a reproducible test over 5 turns.

🔗 Quick-Start Downloads (60 sec)

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

Explore More

Layer Page What its for
Proof WFGY Recognition Map External citations, integrations, and ecosystem proof
⚙️ Engine WFGY 1.0 Original PDF tension engine and early logic sketch (legacy reference)
⚙️ 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
🧰 App Blur Blur Blur Text to image generation with semantic control
🏡 Onboarding Starter Village Guided entry point for new users

If this repository helped, starring it improves discovery so more builders can find the docs and tools.
GitHub Repo stars