Category: Technology Engineering

Introduction: How do you know if your LLM application is actually working? Evaluation is one of the most challenging aspects of building AI systems—unlike traditional software where tests pass or fail, LLM outputs exist on a spectrum of quality. This guide covers the essential metrics, benchmarks, and tools for evaluating LLMs, from automated metrics like […]

Introduction: Hardcoded prompts are a maintenance nightmare. When prompts are scattered across your codebase as string literals, updating them requires code changes, testing, and deployment. Prompt templates solve this by separating prompt logic from application code. This guide covers building a robust prompt template system: variable substitution, conditional sections, template inheritance, version control, and A/B […]

Introduction: LLM APIs fail in ways traditional APIs don’t—rate limits, content filters, malformed outputs, timeouts on long generations, and model-specific quirks. Building resilient LLM applications requires comprehensive error handling: retry logic with exponential backoff, fallback strategies when primary models fail, circuit breakers to prevent cascade failures, and graceful degradation for user-facing applications. This guide covers […]

Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request caps. Hit these limits and your application grinds to a halt with 429 errors. Worse, aggressive retry logic can trigger longer cooldowns. Proper rate limiting isn’t just about staying under limits; it’s about maximizing throughput while gracefully handling bursts, prioritizing […]

Introduction: LLM applications are notoriously difficult to debug. Unlike traditional software where errors are obvious, LLM issues manifest as subtle quality degradation, unexpected costs, or slow responses. Proper observability is essential for production LLM systems. This guide covers monitoring strategies: tracking latency, tokens, and costs; implementing distributed tracing for complex chains; structured logging for debugging; […]

Introduction: LLM API calls are expensive and slow—semantic caching reduces both by reusing responses for similar queries. Unlike exact-match caching, semantic caching uses embeddings to find queries that are semantically similar, even if worded differently. This enables cache hits for paraphrased questions, reducing latency from seconds to milliseconds and cutting API costs significantly. This guide […]