Ananya Desai's contributions

Learn how to build .NET multi-model AI applications with Azure OpenAI and open-source LLMs, using intelligent routing, RAG, fallbacks, and cost optimization.

Build AI-powered support engineers with ASP.NET Core, Azure OpenAI, and Azure AI Search for faster resolutions and reduced costs.

Secure your AI systems! Learn to validate prompts, models, and tool calls for LLMs, RAG, and agents in .NET.

Master AI observability: Track prompts, costs, user feedback, and model performance for reliable, cost-effective production AI.

Boost RAG performance with Azure AI Search Hybrid Retrieval. Combine keyword & vector search for better relevance, accuracy, and fewer hallucinations.

Unlock faster, more reliable software delivery with AI-powered release management. Reduce risk, boost confidence, and optimize your CI/CD.

Optimize AI apps with caching strategies. Reduce latency, cut cloud costs, and boost performance in .NET with techniques like semantic and multi-layer caching.

Explore hybrid AI architecture patterns for integrating AI into traditional apps, enhancing scalability, reliability, and maintainability.

Master AI evaluation for .NET apps. Measure accuracy, reliability, safety, performance, and cost for trustworthy, scalable AI solutions.

Automate Windows workstation setup with WinGet Configuration Files. Achieve consistent, repeatable developer environments for faster onboarding and reduced drift.

AI-powered intelligent terminals boost Windows developer productivity by simplifying command discovery, error resolution, and script generation.

Learn how to design AI-ready data models for enterprise .NET applications using metadata, semantic relationships, vector search, and RAG-friendly retrieval structures.

Master AI Gateway Architecture in ASP.NET Core to manage multiple LLM providers, ensuring flexibility, cost-efficiency, and resilience.

Run AI models locally on Windows with .NET. Explore ONNX Runtime, Ollama, and hardware acceleration for private, fast, and cost-effective AI.

Learn how GitHub Spark uses natural-language prompts to generate application components, support rapid prototyping, and help developers refine and deploy AI-generated solutions.

Build intelligent enterprise knowledge bases with Azure AI Search and ASP.NET Core. Enhance search relevance, productivity, and information access.

Explore Vector Search vs. Semantic Search: understand their core differences, strengths, and when to use each for modern AI applications.

Unlock efficient local development for Azure Cosmos DB on Linux with the emulator. Test queries, data models, and CI/CD workflows.

Learn how to build offline AI applications in .NET using local models such as Phi and runtimes like Ollama, without cloud APIs or internet access.

Explore the most important AI-native architecture patterns for .NET developers, including RAG, Agentic RAG, AI agents, memory systems, workflow orchestration, and event-driven AI.

Learn how to build event-driven AI agents using Azure Functions and Semantic Kernel. Discover serverless AI architectures, event processing patterns, and best practices for enterprise .NET applications.

Learn how to implement AI memory systems in C# using vector databases. Discover embeddings, semantic search, memory architectures, and best practices for building intelligent AI applications.

Learn how to implement structured outputs in C# AI applications. Discover schema validation, strongly typed models, JSON responses, and best practices for building reliable enterprise LLM solutions.

Learn the most important AI agent security risks including prompt injection, data leakage, excessive permissions, and secure AI development best practices.

Learn .NET Aspire and discover how it simplifies building cloud-native applications with service discovery, observability, health checks, and distributed application support.