Fact vs RAG: When to Use Which

architecture

Fact vs RAG: When to Use Which

Why hardcoded facts often beat RAG retrieval for Claude Code workflows. Decision rules, hybrid patterns, cost tradeoffs.

Fact vs RAG: When to Use Which

FACT: Kill Your Vector Database

Why deterministic prompt caching beats fuzzy embedding search: 42 ms vs 2-5 seconds, 93 % cheaper.

The Two Approaches

RAG vs FACT, Defined

Two fundamentally different strategies for giving an LLM access to external knowledge. Understanding the mechanics of each is the prerequisite for every decision below.

Embed all documents into vectors: Chunk text, send each chunk through an embedding model, store in Pinecone / Weaviate / Chroma.

Embed the incoming question: Same model converts your question into a vector at query time.

Cosine similarity search: Find the “closest” vectors. Returns something like what you asked, not exactly what you asked.

Inject top-K chunks into context: Stuff retrieved chunks into the prompt, then generate the answer.

No embeddings, no vector DB: Zero embedding API calls. No vector space, no dimensionality, no cosine math.

Deterministic MCP tool calls: “Run this exact SQL. Return this live API result.” The answer is the current truth.

Prompt caching for static context: Schemas, docs, and config are cached at the prompt level, reused across calls without re-sending tokens.

Exact results, 42 ms: Deterministic queries return precise data. Not “something close”: the actual answer.

When Facts Win

Four Scenarios Where Hardcoded Facts Beat RAG

RAG adds latency, cost, and probabilistic noise to problems that don't need it. If your data fits any of these four profiles, skip the vector database entirely.

When RAG Wins

Three Scenarios Where RAG Is the Right Tool

RAG is not dead. It is just misapplied most of the time. These are the cases where semantic similarity search is genuinely the correct approach.

Hybrid Patterns

Using Both Together

The best production systems don't pick one. They route intelligently. Structured queries go to FACT; fuzzy semantic search goes to RAG; the orchestration layer decides which path each question takes.

Core Concept

FACT's Tiered Caching Architecture

Three cache tiers with different TTLs. The right data gets the right caching strategy: static context is cached for hours, live data is read fresh on every call.

Head to Head

Cost & Performance Comparison

Every metric that matters. FACT wins on latency, cost, accuracy, and freshness. RAG wins on unstructured semantic coverage.

Decision Framework

Decision Tree: FACT or RAG?

Five questions. Answer them top to bottom. The first match is your answer.

Structured data with clear schemas and relationships is ideal for deterministic queries. A SQL WHERE clause beats cosine similarity every time.

Live data will be stale in any vector index. FACT reads from source on demand, so the answer is always current.

Embedding API calls at scale add up. FACT's caching approach costs 85-93 % less per query and returns results in 42 ms.

A fixed knowledge base (product docs, FAQs, historical archives) is RAG's sweet spot. Embed once, query forever. No re-indexing pipeline.

Research papers, legal docs, support tickets where 'close enough' is the goal and exact field matches are impossible. This is what embeddings were built for.

In Practice

Real-World Applications

Four live use cases. Every one replaced a slower RAG-based approach with a FACT tool call.

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