performance-optimization

Performance Optimization

Overview

Use this parent skill when the RAG system works functionally but is too slow, expensive, or unstable under expected traffic. Route to targeted latency and retrieval optimization guidance.

Problem Statement

RAG latency can come from embedding calls, vector search, metadata filters, reranking, prompt assembly, or repeated work. Optimization requires profiling the full retrieval path before changing architecture.

Key Concepts

• Latency budget: Allocate time across embedding, retrieval, reranking, and generation.
• Caching: Avoid repeated work for common queries and stable documents.
• Index tuning: Match search parameters and indexes to workload.
• Query planning: Reduce unnecessary retrieval and reranking work.

Implementation Guide

Step 1: Profile the Retrieval Path

Measure embedding, search, filtering, reranking, prompt assembly, and model latency separately.

Step 2: Apply Targeted Optimizations

Use caching, batching, payload indexes, top-k tuning, and reranker gating where profiling shows bottlenecks.

Step 3: Recheck Quality and Cost

Confirm optimizations do not reduce recall, faithfulness, citation quality, or operational reliability.

When to Use This Skill

• Retrieval is too slow for the product experience
• Reranking or query expansion adds too much latency
• Metadata filters or vector indexes need tuning

When NOT to Use This Skill

• Retrieval quality is poor but latency is acceptable
• The corpus has not been chunked or indexed yet
• The system lacks baseline measurements

Related Skills

• Optimize Retrieval Latency
• Qdrant for Production RAG
• Multi-Pass Retrieval with Reranking

Metrics & Success Criteria

• Lower p50 and p95 retrieval latency
• Stable answer quality after optimization
• Reduced cost per query without hiding failures