Category: Emerging Technologies

Introduction: LLM API calls are expensive and slow. A single GPT-4 request can cost cents and take seconds—multiply that by thousands of users and costs spiral quickly. Caching is the most effective way to reduce both cost and latency. But LLM caching is different from traditional caching: exact string matches are rare, and semantically similar… Continue reading

Introduction: Context windows are limited and tokens are expensive. Long prompts with extensive context, examples, or retrieved documents quickly hit limits and drive up costs. Prompt compression techniques reduce token count while preserving the information LLMs need to generate quality responses. This guide covers practical compression strategies: token pruning to remove low-information tokens, extractive summarization… Continue reading

Introduction: RAG systems live or die by their chunking strategy. Chunk too large and you waste context window space with irrelevant content. Chunk too small and you lose semantic coherence, making it hard for the LLM to understand context. The right chunking strategy depends on your document types, query patterns, and retrieval approach. This guide… Continue reading

Introduction: LLM APIs are inherently slow—even fast models take hundreds of milliseconds per request. When you need to process multiple prompts, make parallel API calls, or handle high-throughput workloads, synchronous code becomes a bottleneck. Async patterns let you overlap I/O wait times, dramatically improving throughput without adding complexity. This guide covers practical async patterns for… Continue reading

Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request limits. Hit these limits and your application grinds to a halt with 429 errors. Effective rate limiting isn’t just about staying under limits; it’s about maximizing throughput while maintaining reliability. This guide covers practical rate limiting patterns: token bucket algorithms… Continue reading