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 […]
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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 […]
Read more →Prompt Debugging Techniques: Systematic Approaches to Fixing LLM Failures
Introduction: Prompt debugging is an essential skill for building reliable LLM applications. When prompts fail—producing incorrect outputs, hallucinations, or inconsistent results—systematic debugging techniques help identify and fix the root cause. Unlike traditional software debugging where you can step through code, prompt debugging requires understanding how language models interpret instructions and where they commonly fail. This […]
Read more →Prompt Templates and Management: Building Maintainable LLM Applications
Introduction: As LLM applications grow in complexity, managing prompts becomes a significant engineering challenge. Hard-coded prompts scattered across your codebase make iteration difficult, A/B testing impossible, and debugging a nightmare. Prompt template management solves this by treating prompts as first-class configuration—versioned, validated, and dynamically rendered. A good template system separates prompt logic from application code, […]
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What is EEHRxF? EEHRxF vs IPS vs eHDSI EEHRxF Architecture EEHRxF Clinical Dataset Ireland’s EEHRxF Readiness FHIR Implementation Example Standards and References Series Conclusion Related Articles in This Series Conclusion
Read more →Production Deployment on Google Cloud – Part 4 of 5
Deploy ADK agents to production on Google Cloud. Complete guide covering Cloud Run, Vertex AI Agent Engine, and GKE deployments with observability, security, and CI/CD pipelines.
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