Tips for building performant LLM applications (moduloware.ai)

Modulo AI published a practical guide, "Writing High-Performance AI Agents in Python," that distills engineering patterns and trade-offs for building fast, cost‑effective LLM applications. The report focuses on production realities—latency, throughput, cost, and correctness—rather than purely model capability, and presents concrete tactics for squeezing performance out of both hosted and on‑device models while keeping agents reliable and debuggable. Key technical takeaways include matching model size to task and hardware (smaller quantized models for low-latency endpoints, larger models where quality matters), using batching and asynchronous I/O to maximize throughput, and streaming responses to reduce perceived latency. Retrieval‑augmented generation and condensed memory/chunking strategies are recommended to keep context windows manageable and reduce token usage; efficient embedding pipelines and vector stores are emphasized for retrieval speed. Other operational patterns cover caching, backoff/retry and circuit breakers for resilience, deterministic testing and logging for observability, and cost/perf profiling to guide architecture choices. Overall the guide stresses pragmatic engineering: instrument everything, measure trade-offs, and co‑optimize prompt design, model choice, and system architecture to build LLM agents that are fast, affordable, and robust in real deployments.