Introduction: Production LLM applications require comprehensive logging and tracing to debug issues, monitor performance, and understand user interactions. Unlike traditional applications, LLM systems have unique logging needs: capturing prompts and responses, tracking token usage, measuring latency across chains, and correlating requests through multi-step workflows. This guide covers practical logging patterns: structured request/response logging, distributed tracing […]
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Guardrails and Safety for LLMs: Building Secure AI Applications with Input Validation and Output Filtering
Introduction: Production LLM applications need guardrails to ensure safe, appropriate outputs. Without proper safeguards, models can generate harmful content, leak sensitive information, or produce responses that violate business policies. Guardrails provide defense-in-depth: input validation catches problematic requests before they reach the model, output filtering ensures responses meet safety standards, and content moderation prevents harmful generations. […]
Read more →Embracing Cloud Native and Multi-Cloud: A Journey Through Modern Infrastructure
The evolution of cloud computing has fundamentally transformed how we architect, deploy, and manage enterprise applications. After spending over two decades in software engineering and solutions architecture, I’ve witnessed this transformation firsthand—from the early days of virtualization to today’s sophisticated cloud-native and multi-cloud ecosystems. This article explores the principles, patterns, and practical considerations that define […]
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 →Prompt Injection Defense: Protecting LLM Applications from Adversarial Inputs
Introduction: Prompt injection is the SQL injection of the AI era. Attackers craft inputs that manipulate your LLM into ignoring instructions, leaking system prompts, or performing unauthorized actions. As LLMs gain access to tools, databases, and APIs, the attack surface expands dramatically. A successful injection could exfiltrate data, execute malicious code, or compromise your entire […]
Read more →Agent Memory Patterns: Building Persistent Context for AI Agents
Introduction: Memory is what transforms a stateless LLM into a persistent, context-aware agent. Without memory, every interaction starts from scratch—the agent forgets previous conversations, learned preferences, and accumulated knowledge. But implementing memory for agents is more complex than simply storing chat history. You need short-term memory for the current task, long-term memory for persistent knowledge, […]
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