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 […]
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Multi-turn Conversation Design: Building Natural Dialogue Systems with LLMs
Introduction: Multi-turn conversations are where LLM applications become truly useful. Users don’t just ask single questions—they refine, follow up, reference previous context, and expect the assistant to remember what was discussed. Building effective multi-turn systems requires careful attention to context management, history compression, turn-taking logic, and graceful handling of topic changes. This guide covers practical […]
Read more →LLM Application Monitoring: Metrics, Tracing, and Alerting for Production AI Systems
Introduction: LLM applications fail in ways traditional software doesn’t. A model might return syntactically correct but factually wrong responses. Latency can spike unpredictably. Costs can explode without warning. Token usage varies wildly based on input. Traditional APM tools miss these LLM-specific failure modes. This guide covers comprehensive monitoring for LLM applications: tracking latency, tokens, and […]
Read more →Enterprise PostgreSQL on Google Cloud: AlloyDB Architecture for Mission-Critical Workloads
Google Cloud AlloyDB provides a fully managed, PostgreSQL-compatible database service designed for demanding enterprise workloads. This comprehensive guide explores AlloyDB’s enterprise capabilities with production-ready examples. AlloyDB Disaggregated Architecture AlloyDB Architecture: Cloud-Native PostgreSQL AlloyDB separates compute and storage into independent layers, enabling each to scale independently. The compute layer runs PostgreSQL-compatible database instances, while the storage […]
Read more →LLM Model Selection: Choosing the Right Model for Every Task
Introduction: Choosing the right LLM for your task is one of the most impactful decisions you’ll make. Use a model that’s too small and you’ll get poor quality. Use one that’s too large and you’ll burn through budget while waiting for slow responses. The landscape changes constantly—new models launch monthly, pricing shifts, and capabilities evolve. […]
Read more →Error Handling in LLM Applications: Retry, Fallback, and Circuit Breakers
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 […]
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