Introduction: Context window limits are one of the most frustrating constraints when building LLM applications. You have a 100-page document but only 8K tokens of context. You want to include conversation history but it’s eating into your prompt budget. Context compression techniques solve this by reducing the token count while preserving the information that matters. […]
Read more →Month: January 2025
What is Landing Zone in Azure? How to implement it via Terraform
In Azure, a landing zone is a pre-configured environment that provides a baseline for hosting workloads. It helps organizations establish a secure, scalable, and well-managed environment for their applications and services. A landing zone typically includes a set of Azure resources such as networks, storage accounts, virtual machines, and security controls. Implementing a landing zone […]
Read more →Azure Service Bus: A Solutions Architect’s Guide to Enterprise Messaging
In the landscape of enterprise application development, reliable messaging infrastructure often determines the difference between systems that gracefully handle load spikes and those that collapse under pressure. Azure Service Bus represents Microsoft’s fully managed enterprise message broker, offering capabilities that extend far beyond simple message queuing. After implementing Service Bus across numerous enterprise integrations, I’ve […]
Read more →LLM Inference Optimization: Caching, Batching, and Smart Routing (Part 1 of 2)
Introduction: LLM inference can be slow and expensive, especially at scale. Optimizing inference is crucial for production applications where latency and cost directly impact user experience and business viability. This guide covers practical optimization techniques: semantic caching to avoid redundant API calls, request batching for throughput, streaming for perceived latency, model quantization for self-hosted models, […]
Read more →Multimodal AI Applications: Building Systems That See, Hear, and Understand
Introduction: Multimodal AI processes and generates content across multiple modalities—text, images, audio, and video. This capability enables applications that were previously impossible: describing images, generating images from text, transcribing and understanding audio, and creating unified experiences that combine all these modalities. This guide covers the practical aspects of building multimodal applications: vision-language models for image […]
Read more →Semantic Kernel: Microsoft’s Enterprise SDK for Building AI-Powered Applications
Introduction: Semantic Kernel is Microsoft’s open-source SDK for integrating Large Language Models into applications. Originally developed to power Microsoft 365 Copilot, it has evolved into a comprehensive framework for building AI-powered applications with enterprise-grade features. Unlike other LLM frameworks that focus primarily on Python, Semantic Kernel provides first-class support for both C# and Python, making […]
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