Introduction: Getting reliable, structured data from LLMs is one of the most practical challenges in building AI applications. Whether you’re extracting entities from text, generating API parameters, or building data pipelines, you need JSON that actually parses and validates against your schema. This guide covers the evolution of structured output techniques—from prompt engineering hacks to […]
Read more →Category: Artificial Intelligence(AI)
Context Compression Techniques: Fitting More Information into Limited Token Budgets
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 →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 →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 […]
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 →LLM Chain Debugging: Tracing, Inspecting, and Fixing Multi-Step AI Workflows
Introduction: Debugging LLM chains is fundamentally different from debugging traditional software. When a chain fails, the problem could be in the prompt, the model’s interpretation, the output parsing, or any of the intermediate steps. The non-deterministic nature of LLMs means the same input can produce different outputs, making reproduction difficult. Effective chain debugging requires comprehensive […]
Read more →