Introduction: Chatbots and conversational AI need memory. Without conversation history, every message exists in isolation—the model can’t reference what was said before, follow up on previous topics, or maintain coherent multi-turn dialogues. But history management is tricky: context windows are limited, old messages may be irrelevant, and naive approaches quickly hit token limits. This guide […]
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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 →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 →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 →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 →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 […]
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