Introduction: Context windows define how much information an LLM can process at once—from 4K tokens in older models to 128K+ in modern ones. Effective context management means fitting the most relevant information within these limits while leaving room for generation. This guide covers practical context window strategies: token counting and budget allocation, content prioritization, compression […]
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Memory Systems for LLMs: Buffers, Summaries, and Vector Storage
Introduction: LLMs have no inherent memory—each request starts fresh. Building effective memory systems enables conversations that span sessions, personalization based on user history, and agents that learn from past interactions. Memory architectures range from simple conversation buffers to sophisticated vector-based long-term storage with semantic retrieval. This guide covers practical memory patterns: conversation buffers, sliding windows, […]
Read more →LLM Prompt Templates: Building Maintainable Prompt Systems
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 […]
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 →LLM Observability: Monitoring AI Applications in Production
Last month, our LLM application started giving wrong answers. Not occasionally—systematically. The problem? We had no visibility. No logs, no metrics, no way to understand what was happening. That incident cost us a major client and taught me that observability isn’t optional for LLM applications—it’s survival. ” alt=”LLM Observability Architecture” style=”max-width: 100%; height: auto; border-radius: […]
Read more →LLM Monitoring and Observability: Metrics, Traces, and Alerts
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; […]
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