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 […]
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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 →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 […]
Read more →The Vibe Coding Revolution: How AI Assistants Are Redefining Developer Productivity in 2025
The term “vibe coding” emerged organically from developer communities in late 2024, describing a new paradigm where programmers collaborate with AI assistants not just for code completion, but for entire feature implementations. Workflow Revolution: Traditional vs Vibe Coding Understanding the Vibe Coding Paradigm Vibe coding represents a fundamental shift in how developers interact with their […]
Read more →Structured Generation Techniques: Getting Reliable JSON from LLMs
Introduction: Getting LLMs to output valid JSON, XML, or other structured formats is surprisingly difficult. Models hallucinate extra fields, forget closing brackets, and produce malformed output that breaks downstream systems. Prompt engineering helps but doesn’t guarantee valid output. This guide covers techniques for reliable structured generation: using native JSON mode and structured outputs, constrained decoding […]
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