Software Development News

A deep, production-minded guide to observability for LLM systems, covering LLM metrics, distributed tracing, logs, profiling, synthetic testing, SLOs, and an LLM observability tools comparison (Prometheus, Grafana, OpenTelemetry, Jaeger/Tempo, Loki/ELK, DCGM, and major APM platforms). Observability for LLM Systems: Metrics, Traces, Logs, and Testing in Production

A rigorous, engineering‑first guide to chunking for RAG: fixed vs semantic vs hierarchical chunking, evaluation dimensions, decision matrix, and runnable Python implementations with FAISS/Chroma/Weaviate and OpenAI embeddings. Chunking Strategies in RAG Comparison: Alternatives, Trade‑offs, and Examples

A detailed comparison of tmux and Zellij, highlighting architecture, features, performance, and usability to help developers choose the best terminal multiplexer for their workflow. Terminal Multiplexers: tmux vs Zellij – A Comprehensive Comparison

Deep dive into WeKnora, a Go-based RAG framework for building scalable, secure, and high-performance retrieval-augmented generation systems with advanced agent capabilities and hybrid retrieval strategies. Using Go to Build RAG Systems: WeKnora Deep Dive

Ollama CLI cheatsheet: ollama serve command, ollama run command examples, ollama ps, and model management. Ollama CLI Cheatsheet: ls, serve, run, ps + commands (2026 update)

Learn how to build high-throughput network clients in Go using concurrency, non-blocking I/O, and modern libraries like gRPC and HTTP/2 for optimal performance and scalability. Writing High-Throughput Network Clients in Go

Learn how to run large language models locally for enhanced data privacy. This guide covers hardware requirements, software frameworks, quantization techniques, and security measures to protect sensitive data in on-premises deployments. Running LLMs Locally for Data Privacy

  This Retrieval-Augmented Generation (RAG) tutorial is a step-by-step, production-focused guide to building real-world RAG systems. If you are searching for: How to build a RAG system RAG architecture explained RAG tutorial with examples How to implement RAG with vector databases RAG with reranking RAG with web search Production RAG best practices You are in the right place. This guide consolidates practical RAG implementation knowledge, architectural patterns, and optimization techniques used in production AI systems. What Is Retrieval-Augmented Generation (RAG)? Retrieval-Augmented Generation (RAG) is a system design pattern that combines: Information retrieval Context augmentation Large language model generation In simple terms, a RAG pipeline retrieves relevant documents and injects them into the prompt before the model generates an answer. Unlike fine-tuning, RAG : Works with frequently updated data Supports private knowledge bases Reduces hallucination Avoids re...