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 →Category: Emerging Technologies
Emerging technologies include a variety of technologies such as educational technology, information technology, nanotechnology, biotechnology, cognitive science, psychotechnology, robotics, and artificial intelligence.
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 →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.
Read more →ML.NET for Custom AI Models: When to Use ML.NET vs Cloud APIs
Six months ago, I faced a critical decision: build a custom ML model with ML.NET or use cloud APIs. The project required real-time fraud detection with zero latency tolerance. Cloud APIs were too slow. ML.NET was the answer. But when should you use ML.NET vs cloud APIs? After building 15+ production ML systems, here’s what […]
Read more →LLM Application Logging and Tracing: Building Observable AI Systems
Introduction: Production LLM applications require comprehensive logging and tracing to debug issues, monitor performance, and understand user interactions. Unlike traditional applications, LLM systems have unique logging needs: capturing prompts and responses, tracking token usage, measuring latency across chains, and correlating requests through multi-step workflows. This guide covers practical logging patterns: structured request/response logging, distributed tracing […]
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