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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DevSecOps: Integrating Security into DevOps – Part 5
Continuing from my previous blog, let’s explore some more advanced topics related to DevSecOps implementation. Identity and Access Management Identity and Access Management (IAM) is a critical aspect of DevSecOps. It involves managing user identities and controlling their access to resources based on their roles and responsibilities. IAM includes the following activities: Infrastructure as Code […]
Read more →Mastering Google Cloud Dataflow: Building Unified Batch and Streaming Pipelines at Scale
Introduction: Google Cloud Dataflow provides a fully managed, serverless data processing service built on Apache Beam that unifies batch and streaming pipelines. This comprehensive guide explores Dataflow’s enterprise capabilities, from pipeline design patterns and windowing strategies to autoscaling, cost optimization, and production monitoring. After building data pipelines processing terabytes daily across multiple cloud providers, I’ve […]
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 […]
Read more →Privacy-Preserving AI: Techniques for Sensitive Data
Last year, we trained a model on customer data. A researcher showed they could reconstruct customer information from model outputs. After implementing privacy-preserving techniques across 10+ projects, I’ve learned how to protect sensitive data while enabling AI capabilities. Here’s the complete guide to privacy-preserving AI. Figure 1: Privacy-Preserving AI Techniques Overview Why Privacy-Preserving AI Matters: […]
Read more →LLM Error Handling: Building Resilient AI Applications
Introduction: LLM APIs fail. Rate limits get hit, servers time out, responses get truncated, and models occasionally return garbage. Production applications need robust error handling that gracefully recovers from failures without losing user context or corrupting state. This guide covers practical error handling strategies: detecting and classifying different error types, implementing retry logic with exponential […]
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