Instiq
Chapter 4 · Operational Efficiency and Optimization for GenAI Applications·v1.0.0·Updated 6/22/2026·~11 min

What's changed: Initial: 3 sections for Domain 4 (operational efficiency and optimization)

4.2Optimizing Application Performance

Key points

Learn to improve GenAI app responsiveness: latency-optimized models, parallel requests, streaming, tuning temperature/top-k/top-p, retrieval optimization, and Auto Scaling.

Generation is inherently slow. Optimize UX by reducing perceived latency, increasing throughput, and tuning output quality via parameters.

4.2.1Levers to improve performance

  • Lower latency: latency-optimized models, streaming, pre-computation for predictable queries, and parallel requests for complex workflows.
  • Output parameters: tune temperature/top-k/top-p to requirements (low temperature for determinism, higher for diversity).
  • Retrieval optimization: speed/accuracy of RAG via index optimization, query preprocessing, and custom-scored hybrid search.
  • Throughput/scale: token-processing optimization, batch inference, concurrent-invocation management, and Auto Scaling for GenAI traffic.

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