The 8 RAG Architectures and Why They All Share the Same Blind Spot

If you've built anything with LLMs in the past two years, you've probably implemented some form of RAG (Retrieval-Augmented Generation). You embed documents, store vectors, retrieve relevant chunks, and feed them to your model.

It works. Until it doesn't.

The problem isn't retrieval. The problem is that your system has no memory of its mistakes. Correct it once, and it forgets. Correct it a hundred times, and it still pulls the same wrong context with the same confidence.

But before we get to the fix, let's understand where we are.

The 8 RAG Architectures

Not all RAG is created equal. The field has evolved rapidly, and today there are roughly eight distinct approaches—each solving different problems, each with different tradeoffs.

Each of these architectures represents real progress. Graph RAG handles relationships that flat retrieval can't. Self-RAG catches errors before they reach the user. Agentic RAG adapts to complex, multi-step queries.

But they all share the same fundamental limitation.

Why This Matters

Consider a simple personal assistant:

// Turn 1
User: "My favorite color is blue."
Agent: Stores vector with content "user favorite color blue"

// Turn 47
User: "Actually, my favorite color is red now. You got that wrong."
Agent: Stores NEW vector "user favorite color red"
       // But "blue" vector still exists at full relevance

// Turn 48
User: "What's my favorite color?"
Agent: Retrieves BOTH vectors. Picks "blue" (older, more embeddings).
       "Your favorite color is blue!"
User: 😑

This isn't a retrieval problem. Both vectors are semantically relevant to "favorite color." The system correctly found related context.

It's a trust problem. The system has no way to know that one piece of context has been explicitly invalidated by the user.

Current "Solutions" and Why They Fail

Approach	What It Does	Why It Fails
Delete wrong memory	Remove the incorrect vector entirely	Catastrophic forgetting. Lose all context, can't audit, can't resurrect if deletion was wrong.
Override with new entry	Add new vector, hope it ranks higher	Creates duplicates. Old entry still retrieved. No guarantee new one wins.
Time-based decay	Reduce relevance of older memories	Punishes everything old, not just wrong things. "My birthday" shouldn't decay.
Ignore corrections	Hope users don't notice	They notice. They leave.

None of these approaches target the actual problem: surgically reducing trust in specific memories that have been proven wrong, while leaving everything else intact.

SVTD: Surgical Vector Trust Decay

SVTD (Surgical Vector Trust Decay) is a memory primitive that sits beneath any RAG architecture. It doesn't replace your retrieval—it adds a trust layer on top of it.

How It Works

Note: If correction is ambiguous, SVTD stages the intent and waits for clarification on the next turn.

Step 1: Passive Detection

A lightweight "Sentinel" monitors conversations for correction patterns—"No, that's wrong," "Actually, it's X not Y," "You got that mixed up." No explicit feedback buttons needed. The system learns from natural conversation.

Step 2: Surgical Targeting

When a correction is detected, semantic disambiguation finds the specific memory being contradicted. The system uses multiple signals: question numbers (Q1, Q2), entity matching (BB, kids), domain overlap (family vs work), and lexical matching. Not all memories about the topic—just the one that conflicts with the correction.

Step 2.5: Correction Intent Staging (New)

If a correction is ambiguous (e.g., "that's wrong" without context), SVTD stages the correction intent in an ephemeral holding area. If the next user input provides clarification (question numbers, entities, domain terms), the staged correction is bound to specific memories and decay is applied. This prevents silent failures while maintaining safety-first design.

Step 3: Trust Decay

The targeted memory's trust weight drops. For explicit corrections: aggressive decay (×0.01). For user-flagged memories: escalating penalty curve (1st: -0.10, 2nd: -0.25, 3rd: -0.35). Third strike: memory enters "ghost state"—suppressed from retrieval but preserved for audit.

# Retrieval with SVTD
final_score = cosine_similarity * trust_weight

# Memory A: "favorite color blue" (trust: 0.25 after corrections)
score_A = 0.92 * 0.25 = 0.23

# Memory B: "favorite color red" (trust: 1.0, newly stored)
score_B = 0.89 * 1.0 = 0.89

# Red wins. System answers correctly.

Step 4: Non-Destructive Suppression

Nothing is deleted. Low-trust memories are suppressed, not erased. If positive feedback later validates a suppressed memory, trust can be restored. The system maintains full audit trail.

Privacy Mode (External Deployments)

For SOC2 by design deployments, SVTD operates in metadata-only mode. The system uses topics, entities, domain tags, and sequence numbers for disambiguation—without retaining user content. Designed for compliance with zero content retention.

Why "Surgical" Matters

The key insight is precision. When you tell your assistant "No, I work at Acme Corp, not Beta Inc," only the "Beta Inc" memory should be affected. Your birthday, your preferences, your project history—all stay at full trust.

Multi-Correction Handling: When you correct multiple items in one message (e.g., "Q1 is correct, Q2 is wrong, Q3 is also wrong"), SVTD maps each correction to specific memories using sequence numbers (Q1→Memory [1], Q2→Memory [2]) and entity matching. Only explicitly contradicted memories decay—nothing more.

Compare this to time-based decay, which would gradually suppress all old memories equally, or deletion, which loses the context entirely.

SVTD targets exactly what's wrong. Nothing more.

Integration: One Multiplier

SVTD works with any vector store (Pinecone, Weaviate, Chroma, Qdrant) and any RAG architecture. The integration point is simple:

# Your existing retrieval
results = vector_store.query(embedding, top_k=10)

# Add SVTD
for result in results:
    trust = svtd.get_trust_weight(result.id)
    result.score = result.score * trust

# Re-rank by adjusted score
results.sort(key=lambda x: x.score, reverse=True)

That's it. Your Naive RAG becomes trust-weighted Naive RAG. Your Graph RAG becomes trust-weighted Graph RAG. The architecture stays the same—it just gets smarter about what to trust.

The Primitive Layer

We call this "Naive SVTD" intentionally. Just as Naive RAG (2020) established the baseline for retrieval-augmented generation, Naive SVTD establishes the baseline for trust-weighted memory.

The variants are coming: Graph SVTD for relationship-aware trust propagation. Multimodal SVTD for image/video context. Hierarchical SVTD for document-level vs. chunk-level trust.

But the primitive is ready now.

What Changes

With SVTD, your agents:

• Learn from corrections automatically. No explicit feedback buttons. Natural conversation drives trust updates.
• Stop repeating mistakes. Corrected memories get suppressed. New information surfaces naturally.
• Maintain full context. Nothing is deleted. Audit trails preserved. Resurrection possible if trust is restored.
• Get smarter over time. Every interaction refines what the system trusts. Accuracy compounds.

This is the missing layer. Not better retrieval—better memory.