Documents QAG
NVIDIA's RAG blueprint, remade deterministic
NVIDIA RAG fork: Q-Prime encoding + QAG Engine reasoning. Probabilistic retrieval → deterministic QAG.
Same pipeline surface. Deterministic core.
Re-spine the core — keep ingestion, API, and tooling. Swap encoding to Q-Prime and reasoning to QAG.
Classical NVIDIA RAG
Retrieve similar chunks → LLM
- 1.Chunk corpus. SBERT embeddings.
- 2.Top-k cosine similarity.
- 3.Stuff chunks into context.
- 4.Generate. Hope for the best.
Risk
Conflicts and gaps surface after generation — if at all.
Documents QAG
Q-Prime → HSC signals → QAG Engine
- 1.Q-Prime encodes polarity, scope, dependencies.
- 2.Relevance + Conflict, Overlap, Coverage signals.
- 3.QAG Engine evaluates against scope and time frame.
- 4.Generator receives named structure — responds deterministically.
Outcome
Signals surface before generation — visible to auditors.
Six capabilities the base blueprint does not ship
Q-Prime encoding
Replaces SBERT — polarity, scope, and dependencies survive encoding.
HSC signals
Seven signals replace cosine-similarity top-k retrieval.
Conflict before generation
Policy disagreements emit Conflict signals — not silent averaging.
Coverage gaps named
Missing overlays flagged as Coverage — route to curation, not hallucination.
Replay-grade provenance
Source, version, validity window on every reasoning input.
Drop-in migration
Swap encoding and reasoning; keep NVIDIA ingestion and API surface.
First home: Financial Services
Anchor blueprint for FS and insurance — mortgage overlays, underwriting guidelines, policy intake.
Questions teams ask before they migrate from NVIDIA RAG.
What is Documents QAG?
How is this different from classical NVIDIA RAG?
Full rewrite or migration?
Which industries?
How do I evaluate?
Already running the NVIDIA RAG blueprint?
Migrate encoding and reasoning in place. Keep your ingestion and observability.