Category: Technology Engineering

Introduction: Vector embeddings are the foundation of modern AI applications—from semantic search to RAG systems to recommendation engines. They transform text, images, and other data into dense numerical representations that capture semantic meaning, enabling machines to understand similarity and relationships in ways that traditional keyword matching never could. This guide provides a deep dive into… Continue reading

Introduction: Processing thousands or millions of items through LLMs requires different patterns than single-request applications. Naive sequential processing is too slow, while uncontrolled parallelism hits rate limits and wastes money on retries. This guide covers production batch processing patterns: chunking strategies, parallel execution with rate limiting, progress tracking, checkpoint/resume for long jobs, cost estimation, and… Continue reading

Introduction: Fine-tuning adapts pre-trained language models to specific tasks, domains, or behaviors. While prompting works for many use cases, fine-tuning delivers better performance, lower latency, and reduced costs for specialized applications. This guide covers modern fine-tuning approaches: full fine-tuning for maximum customization, LoRA and QLoRA for efficient parameter updates, preparing high-quality training data, using OpenAI… Continue reading

Introduction: LLM outputs are inherently unstructured text, but applications need structured data—JSON objects, typed responses, specific formats. Getting reliable structured output requires careful prompt engineering, output parsing, validation, and error recovery. This guide covers practical output formatting techniques: JSON mode and structured outputs, Pydantic-based parsing, format enforcement with retries, template-based formatting, and strategies for handling… Continue reading

Introduction: LLM agents extend language models beyond text generation into autonomous action. By connecting LLMs to tools—web search, code execution, APIs, databases—agents can gather information, perform calculations, and interact with external systems. This guide covers building tool-using agents from scratch: defining tools with schemas, implementing the reasoning loop, handling tool execution, managing conversation state, and… Continue reading

Introduction: Observability is essential for production LLM applications—you need visibility into latency, token usage, costs, error rates, and output quality. Unlike traditional applications where you can rely on status codes and response times, LLM applications require tracking prompt versions, model behavior, and semantic quality metrics. This guide covers practical observability: distributed tracing for multi-step LLM… Continue reading

Introduction: Natural language to SQL is one of the most practical LLM applications. Business users can query databases without knowing SQL, analysts can explore data faster, and developers can prototype queries quickly. But naive implementations fail spectacularly—generating invalid SQL, hallucinating table names, or producing queries that return wrong results. This guide covers building robust text-to-SQL… Continue reading

Introduction: Knowledge graphs represent information as entities and relationships, enabling powerful reasoning and querying capabilities. LLMs excel at extracting structured knowledge from unstructured text—identifying entities, relationships, and attributes that can be stored in graph databases. This guide covers building knowledge graphs with LLMs: entity and relation extraction, graph schema design, populating Neo4j and other graph… Continue reading

Introduction: Ollama has revolutionized how developers run large language models locally. With a simple command-line interface and seamless hardware acceleration, you can have Llama 3.2, Mistral, or CodeLlama running on your laptop in minutes—no cloud API keys, no usage costs, complete privacy. Built on llama.cpp, Ollama abstracts away the complexity of model quantization, memory management,… Continue reading