H33 vs RAG
Retrieval helps the model answer. Evidence helps you prove the answer.
Retrieval-augmented generation gives an AI system relevant context before it answers. H33 produces cryptographic evidence for why the answer was produced. RAG improves output quality. H33 makes output auditable, replayable, and independently verifiable — by regulators, auditors, insurers, customers, or acquirers — without contacting the AI vendor and without phoning home.
What RAG does
Retrieval-augmented generation is a pattern for improving LLM output quality. Before the model answers, a retrieval step finds relevant documents from a knowledge base and includes them in the prompt. The model then answers with the retrieved context in hand. RAG addresses three problems with raw LLM behavior: stale training data (retrieval brings in fresh context), hallucination (retrieval grounds answers in real documents), and missing enterprise context (retrieval supplies it). When implemented well, output quality improves measurably.
What RAG does not do
RAG does not produce evidence that the answer was generated this way. RAG does not produce a cryptographically signed audit trail. RAG does not produce an artifact that survives the vendor change. RAG does not let a third party verify what happened without trusting the system that produced the output. A RAG system can log retrieved documents and final answers — those logs are useful for debugging, but they are not audit evidence. They can be edited, lost in vendor migrations, or contradicted across systems.
What H33 does
H33 produces evidence bundles for AI decisions. Every bundle contains eight cryptographically-linked evidence control objects covering policy, model identity, authority, abstention, pipeline execution, corpus binding, evidence rows, and citation binding. Bundles are signed by three independent post-quantum algorithm families and can be anchored to public blockchains for proof of existence in time. The bundle is portable. It travels with the customer. Anyone holding the bundle can verify it without contacting H33.
When RAG is sufficient
You do not need H33 if you are building a chatbot or copilot for unregulated workflows, output quality is the only concern, the downstream consumer of the AI output is internal, the output does not affect compliance/legal/regulatory exposure, or you do not need to prove how a decision was made to anyone external. For internal productivity tools, code copilots, customer-service summarization, and similar workloads, RAG alone is the right answer.
When you need both
You need H33 in addition to RAG when the AI output affects a regulated decision (lending, underwriting, healthcare, government benefits, compliance review), an auditor or regulator or insurer will need to verify the decision basis later, the downstream consumer does not trust your internal logs, the output will outlive the AI vendor or model version, or you need to prove what happened without exposing what was retrieved. In these cases, RAG improves the quality of the output and H33 makes the output verifiable. The two work together: the retrieval context becomes part of the H33 evidence bundle's CorpusBind and EvidenceAttestation objects.
Side-by-side
| Dimension | RAG | H33 |
|---|---|---|
| Primary goal | Improve answer quality | Prove answer integrity |
| Output artifact | An LLM response | An evidence bundle |
| Audit trail | System logs (mutable) | Cryptographic bundle (tamper-evident) |
| Verifiability | Trust the system | Verify independently without vendor contact |
| Vendor portability | Output is vendor-coupled | Bundle survives vendor change |
| Post-quantum readiness | N/A | Three-family PQ signatures |
| Anchoring | N/A | Optional public chain anchor (32 bytes) |
| Privacy | Depends on implementation | Bundle stays under customer control |
| Open source verifier | N/A | Yes |
Use cases that need both
A cyber insurance underwriting AI uses RAG to consult policy documents and underwriting guidelines, then produces a coverage decision. RAG improves the decision quality. H33 produces an evidence bundle proving which guidelines were consulted, which model produced the decision, and which policy was in effect — verifiable by the reinsurer downstream without contacting the carrier.
A clinical decision support AI uses RAG to consult patient records, treatment protocols, and recent literature. RAG improves the recommendation quality. H33 produces an evidence bundle proving the basis of every recommendation — verifiable by a malpractice investigator without contacting the hospital's AI vendor.
Common questions
Do I have to choose one?
No. They solve different problems. Use RAG for output quality. Use H33 for output verifiability. The H33 evidence bundle records the RAG retrieval as part of the CorpusBind and EvidenceAttestation objects.
Will H33 slow my RAG system down?
Bundle generation adds milliseconds at decision time. It does not affect retrieval or generation latency.
Does H33 require a specific LLM provider?
No. H33 is model-agnostic. The ModelFingerprint object captures whatever model identity and version your system reports.
Can H33 evidence prove the LLM didn't hallucinate?
The bundle proves what the LLM produced and what evidence it was grounded in. If the model hallucinated against grounded evidence, the bundle exposes that. The bundle makes hallucination detectable.
Does H33 work with agentic systems?
Yes. The PipelineDag object captures multi-step agent execution. Each agent step's input and output digests are recorded in the DAG, producing a verifiable execution trace.
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