Category: Technology Engineering

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… Continue reading

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… Continue reading

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… Continue reading

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;… Continue reading

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… Continue reading

Introduction: LLM API calls are slow—often 1-10 seconds per request. Sequential processing kills throughput. Async patterns let you process multiple requests concurrently, dramatically improving performance for batch operations, parallel tool calls, and high-traffic applications. This guide covers async LLM patterns in Python: using asyncio with OpenAI and Anthropic clients, managing concurrency with semaphores, implementing retry… Continue reading

Introduction: Function calling transforms LLMs from text generators into capable agents that can interact with external systems. By defining tools with clear schemas, models can decide when to call functions, extract parameters from natural language, and incorporate results into responses. This guide covers practical function calling patterns: defining tool schemas, handling multiple tool calls, implementing… Continue reading

Introduction: Fine-tuning transforms a general-purpose LLM into a specialized model tailored to your domain, style, or task. While prompt engineering can get you far, fine-tuning offers consistent behavior, reduced token usage, and capabilities that prompting alone cannot achieve. This guide covers the complete fine-tuning workflow—from data preparation to deployment—using both cloud APIs (OpenAI, Together AI)… Continue reading

Introduction: Vector search powers semantic retrieval in RAG systems, recommendation engines, and similarity search applications. But naive vector search doesn’t scale—searching millions of vectors with brute force is too slow for production. This guide covers optimization techniques: HNSW indexes for fast approximate search, IVF partitioning for large datasets, product quantization for memory efficiency, hybrid search… Continue reading

Introduction: Testing LLM applications presents unique challenges compared to traditional software. Outputs are non-deterministic, quality is subjective, and the same input can produce different but equally valid responses. This guide covers practical testing strategies: unit testing with mocked LLM responses, integration testing with real API calls, evaluation frameworks for quality assessment, and regression testing to… Continue reading