Introduction: Semantic search using embeddings has transformed how we find information. Unlike keyword search, embeddings capture meaning—finding documents about “machine learning” when you search for “AI training.” This guide covers building production embedding search systems: choosing embedding models, computing and storing vectors efficiently, implementing similarity search with various distance metrics, and optimizing for speed and […]
Read more →Batch Processing for LLMs: Maximizing Throughput with Async Execution and Rate Limiting
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 →GPT-4 Turbo and the OpenAI Assistants API: Building Production Conversational AI Systems
Introduction: OpenAI’s DevDay 2023 marked a pivotal moment in AI development with the announcement of GPT-4 Turbo and the Assistants API. These releases fundamentally changed how developers build AI-powered applications, offering 128K context windows, native JSON mode, improved function calling, and persistent conversation threads. After integrating these capabilities into production systems, I’ve found that the […]
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 →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 →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 […]
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