Introduction: Semantic caching revolutionizes how we handle LLM requests by recognizing that similar questions deserve similar answers. Unlike traditional exact-match caching, semantic caching uses embeddings to find queries that are semantically equivalent, returning cached responses even when the wording differs. This can reduce LLM API costs by 30-70% while dramatically improving response latency for common […]
Read more →Visual Studio 2015 Update 3 – Download
Today Microsoft has released Update 3 for Visual Studio 2015. Visual Studio 2015 Update 3 includes a variety of capability improvements and bug fixes. To find out what’s new, see the Visual Studio 2015 Update 3 Release Notes. For a list of fixed bugs and known issues, see the Visual Studio 2015 Update 3 MSDN […]
Read more →.NET Core 1.0 and ASP.NET Core 1.0 released (RTM)
Microsoft has released final version of .NET Core 1.0 and ASP.NET Core 1.0 today. During May 2016, Microsoft has released RC2 version of the same framework hinting the release of final version soon, within a month Microsoft has released final version (Release to Manufacture). With this release, you can start building your next application today […]
Read more →Vector Search Algorithms: From Brute Force to HNSW and Beyond
Introduction: Vector search is the foundation of modern semantic retrieval systems, enabling applications to find similar items based on meaning rather than exact keyword matches. Understanding the algorithms behind vector search—from brute-force linear scan to sophisticated approximate nearest neighbor (ANN) methods—is essential for building efficient retrieval systems. This guide covers the core algorithms that power […]
Read more →LLM Routing and Load Balancing: Optimizing Cost and Performance Across Model Fleets
Introduction: LLM routing and load balancing are critical for building cost-effective, reliable AI systems at scale. Not every query needs GPT-4—many can be handled by smaller, faster, cheaper models with equivalent quality. Intelligent routing analyzes incoming requests and directs them to the most appropriate model based on complexity, cost constraints, latency requirements, and current system […]
Read more →Retrieval Evaluation Metrics: Measuring What Matters in Search and RAG Systems
Introduction: Retrieval evaluation is the foundation of building effective RAG systems and search applications. Without proper metrics, you’re flying blind—unable to tell if your retrieval improvements actually help or hurt end-user experience. This guide covers the essential metrics for evaluating retrieval systems: precision and recall at various cutoffs, Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative […]
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