Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request limits. Exceeding these limits results in 429 errors that can cascade through your application. Effective rate limiting on your side prevents hitting API limits, provides fair access across users, and enables graceful degradation under load. This guide covers practical rate […]
Read more →Category: Technology Engineering
Technology Engineering
Vector Embeddings Deep Dive: From Theory to Production Search Systems
Introduction: Vector embeddings are the foundation of modern AI applications—from semantic search to RAG systems to recommendation engines. They transform text, images, and other data into dense numerical representations that capture semantic meaning, enabling machines to understand similarity and relationships in ways that traditional keyword matching never could. This guide provides a deep dive into […]
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 Batch Processing: Scaling AI Workloads from Hundreds to Millions
Introduction: Processing thousands or millions of items through LLMs requires different patterns than single-request applications. Naive sequential processing is too slow, while uncontrolled parallelism hits rate limits and wastes money on retries. This guide covers production batch processing patterns: chunking strategies, parallel execution with rate limiting, progress tracking, checkpoint/resume for long jobs, cost estimation, and […]
Read more →Vector Search Optimization: The Complete Guide to Embeddings, Indexing, and Hybrid Search
Introduction: Vector search is the foundation of modern RAG systems, but naive implementations often deliver poor results. Optimizing vector search requires understanding embedding models, index types, query strategies, and reranking techniques. The difference between a basic similarity search and a well-tuned retrieval pipeline can be dramatic—both in relevance and latency. This guide covers practical vector […]
Read more →Building Production AI Applications with .NET 8 and C# 12
When .NET 8 and C# 12 were released, I was skeptical. After 15 years building enterprise applications, I’d seen framework updates come and go. But this release changed everything for AI development. Let me show you how to build production AI applications with .NET 8 and C# 12—using actual C# code, not Python wrappers. Figure […]
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