Introduction: Processing thousands of LLM requests efficiently requires batch processing strategies that maximize throughput while respecting rate limits and managing costs. Individual API calls are inefficient for bulk operations—batch processing enables parallel execution, request queuing, and optimized resource utilization. This guide covers practical batch processing patterns: async concurrent execution, request queuing with backpressure, rate-limited batch […]
Read more →Category: Technology Engineering
Technology Engineering
Mastering Prompt Engineering: Advanced Techniques for Production LLM Applications
Introduction: Prompt engineering has emerged as one of the most critical skills in the AI era. The difference between a mediocre AI response and an exceptional one often comes down to how you structure your prompt. After years of working with large language models across production systems, I’ve distilled the most effective techniques into this […]
Read more →Document Processing Pipelines: From Raw Files to Vector-Ready Chunks
Introduction: Document processing is the foundation of any RAG (Retrieval-Augmented Generation) system. Before you can search and retrieve relevant information, you need to extract text from various file formats, split it into meaningful chunks, and generate embeddings for vector search. The quality of your document processing pipeline directly impacts retrieval accuracy and ultimately the quality […]
Read more →LLM Caching Strategies: From Exact Match to Semantic Similarity
Introduction: LLM API calls are expensive and slow. Caching is your first line of defense against runaway costs and latency. But caching LLM responses isn’t straightforward—the same question phrased differently should return the same cached answer. This guide covers caching strategies for LLM applications: exact match caching for deterministic queries, semantic caching using embeddings for […]
Read more →LLM Memory and Context Management: Building Conversational AI That Remembers
Introduction: LLMs have no inherent memory—each API call is stateless. The model doesn’t remember your previous conversation, your user’s preferences, or the context you established five messages ago. Memory is something you build on top. This guide covers implementing different memory strategies for LLM applications: buffer memory for recent context, summary memory for long conversations, […]
Read more →.NET 8 and C# 12: A Deep Dive into Native AOT, Primary Constructors, and Blazor United
Introduction: .NET 8 represents a landmark release in Microsoft’s development platform evolution, bringing Native AOT to mainstream scenarios, unifying Blazor’s rendering models, and introducing C# 12’s powerful new features. Released in November 2023, this Long-Term Support version delivers significant performance improvements, reduced memory footprint, and enhanced developer productivity. After migrating several enterprise applications to .NET […]
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