Introduction: AI agents become powerful when they can use tools—searching the web, querying databases, calling APIs, executing code. But tool selection is where many agent implementations fail. The agent might choose the wrong tool, call tools with incorrect parameters, or get stuck in loops trying tools that won’t work. This guide covers practical patterns for […]
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LLM Output Parsing: Extracting Structured Data from Language Model Responses
Introduction: LLMs generate text, but applications need structured data. Parsing LLM outputs reliably is one of the most common challenges in production systems. The model might return JSON with extra text, miss required fields, use unexpected formats, or hallucinate invalid values. This guide covers practical parsing strategies: using structured output modes, building robust parsers with […]
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 →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, […]
Read more →Tool Use Patterns: Building LLM Agents That Can Take Action
Introduction: Tool use transforms LLMs from text generators into capable agents that can search the web, query databases, execute code, and interact with APIs. But implementing tool use well is tricky—models hallucinate tool calls, pass invalid arguments, and struggle with multi-step tool chains. The difference between a demo and production system lies in robust tool […]
Read more →Multi-Modal LLM Integration: Building Applications with Vision Capabilities
Introduction: Modern LLMs understand more than text. GPT-4V, Claude 3, and Gemini can process images alongside text, enabling applications that reason across modalities. Building multi-modal applications requires handling image encoding, managing mixed-content prompts, and designing interactions that leverage visual understanding. This guide covers practical patterns for integrating vision capabilities: encoding images for API calls, building […]
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