FACT

FACT: Fast Augmented Context Tools

A revolutionary approach to LLM data retrieval that replaces RAG with prompt caching and deterministic tool execution under the Model Context Protocol

TL;DR

FACT (Fast Augmented Context Tools) introduces a new paradigm for language model–powered data retrieval by replacing vector-based retrieval with a prompt-and-tool approach under the Model Context Protocol (MCP). The result? Sub-100ms responses, 60-90% cost reduction, and deterministic, auditable results with no vector stores required.

Why FACT? RAG Had Its Moment. It's Time for Something Smarter.

RAG (Retrieval-Augmented Generation) made sense when vector search was the best we had. But vectors are slow, fuzzy, and expensive to maintain. They're inherently imprecise, forcing you to tune similarity thresholds, re-embed documents, and accept that relevance is always a bit of a guess.

What we needed was something **explicit. Deterministic. Cheap. Fast.**

FACT isn't about fetching similar chunks of data. It's about giving models **structured, exact answers** via tool execution and pairing that with intelligent prompt caching. Prompt caches work like brains with memory. Tools act like hands that do. And when you combine the two—prompt caching + MCP-based tools—you can skip vector search entirely.

Instead of saying "Find me something like this," FACT says: "Run this exact SQL call. Return this live API result. Use this schema. Cache the output."

Introduction to FACT

**FACT (Fast Augmented Context Tools)** introduces a new paradigm for language model–powered data retrieval by replacing vector-based retrieval with a prompt-and-tool approach under the Model Context Protocol (MCP). Instead of relying on embeddings and similarity searches, FACT combines intelligent prompt caching with deterministic tool invocation to deliver fresh, precise, and auditable results.

Key Differences from RAG

FACT represents a fundamental shift from traditional RAG (Retrieval-Augmented Generation) approaches:

**Retrieval Mechanism**

• RAG: Embeddings → Vector search → LLM completion
• FACT: Prompt cache → MCP tool calls → LLM refinement

**Data Freshness**

• RAG: Periodic re-indexing required
• FACT: Live data via on-demand tool execution

**Accuracy**

• RAG: Probabilistic, fuzzy matches
• FACT: Exact outputs from SQL, API, or custom tools

**Cost & Latency**

• RAG: Embedding + lookup + token costs
• FACT: Cache hits eliminate tokens; cache misses trigger fast tool calls

Core Architectural Innovation

Traditional RAG Approach:
User Query → Embedding → Vector Search → Context Retrieval → LLM → Response (2-5 seconds)

FACT MCP Approach:
User Query → Prompt Cache → [If Miss] → MCP Tool Execution → Cache Update → Response (50-200ms)

Agentic Engineering & Intelligent Caching

FACT enables **agentic workflows** where AI systems make intelligent decisions about data retrieval, caching, and tool execution in complex, m