Introduction: Streaming responses dramatically improve perceived latency in LLM applications. Instead of waiting seconds for a complete response, users see tokens appear in real-time, creating a more engaging experience. Implementing streaming correctly requires understanding Server-Sent Events (SSE), handling partial tokens, managing connection lifecycle, and gracefully handling errors mid-stream. This guide covers practical streaming patterns: basic […]
Read more →Month: September 2024
Conversation Design Patterns: Building Natural Chatbot Experiences
Introduction: Effective conversational AI requires more than just calling an LLM—it needs thoughtful conversation design. This includes managing multi-turn context, handling user intent, graceful error recovery, and maintaining consistent personality. This guide covers essential conversation patterns: intent classification and routing, slot filling for structured data collection, conversation state machines, context window management, and building chatbots […]
Read more →Embedding Search and Similarity: Building Semantic Search Systems
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 →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 […]
Read more →OpenAI API Complete Guide: From Chat Completions to Assistants
A comprehensive guide to the OpenAI API covering GPT-4o, function calling, the Assistants API, vision capabilities, and production best practices with code examples.
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