# Why RAG Fails at Microservices Code Review at Scale

Single-service RAG limitations are annoying. At scale, they become architectural blockers.

## The Compounding Problem

Every microservice added creates more cross-service relationships. In a system of N services, potential dependencies scale as N-squared. RAG retrieval gets noisier as services multiply.

## The Chunk Size Trap

Small chunks lose function-level context. Large chunks hit token limits. API contracts get fragmented across chunks.

## Vector Similarity Cannot Model Architecture

Embeddings capture what code is, not how it relates. Event-driven relationships and schema consumers exist in system topology, not in embeddings.

## The Index Lag Problem

At high-velocity organizations, RAG indexes are always stale. Reviewers get answers based on hours-old state.

## False Confidence at Scale

The most dangerous failure: RAG retrieves something, the LLM produces authoritative-looking analysis, but important context was fragmented or missing. At scale, no engineer has full familiarity with 200 services.

## Graph-Based Code Analysis

A code graph represents services as nodes and dependencies as edges. Traversal is deterministic. The answer to 'what services consume this API?' is exact, not approximate.

## About CodeAnt AI

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