A technical walkthrough of RocksDB’s leveled and tiered compaction, with real‑world patterns, performance numbers, and actionable guidance for production engineers.
Introduction Retrieval‑Augmented Generation (RAG) has reshaped how we think about large language models (LLMs). By coupling a generative model with an external knowledge store, RAG lets us answer questions that lie outside the static training data, keep factuality high, and dramatically reduce hallucination. When the knowledge source is visual—product photos, medical scans, design drawings—the problem becomes multi‑modal: the system must retrieve both textual and visual artifacts and fuse them into a coherent answer. Production‑grade vision‑and‑language applications (e.g., visual search assistants, automated report generation from satellite imagery, interactive design tools) demand: ...
Introduction Vector search—sometimes called similarity search or nearest‑neighbor search—has moved from academic labs to the core of modern AI‑powered products. Whether you are powering a recommendation engine, a semantic text‑retrieval system, or an image‑search feature, the ability to find the most similar vectors in a massive dataset in milliseconds is a competitive advantage. In early prototypes, a single‑node index (e.g., FAISS, Annoy, or HNSWlib) often suffices. However, as data volumes grow to billions of vectors, latency requirements tighten, and uptime expectations rise to “five nines,” a monolithic deployment quickly becomes a bottleneck. Scaling out the index across multiple machines while maintaining high availability (HA) introduces a new set of architectural challenges: ...
Introduction Retrieval‑Augmented Generation (RAG) has quickly become the de‑facto pattern for building knowledge‑aware language‑model applications. By coupling a large language model (LLM) with an external knowledge store, developers can overcome the hallucination problem, keep responses up‑to‑date, and dramatically reduce token costs. The next evolutionary step—agentic RAG—adds a layer of autonomy. Instead of a single static retrieval‑then‑generate loop, an agent decides when to retrieve, what to retrieve, which tools to invoke (e.g., calculators, web browsers, code executors), and how to stitch results together into a coherent answer. This architecture mirrors how a human expert works: look up a fact, run a simulation, call a colleague, and finally synthesize a report. ...
Introduction Large language models (LLMs) have transformed how developers build conversational agents, code assistants, and even data‑driven products. Their ability to generate fluent text from massive corpora is undeniable, yet they are fundamentally statistical pattern matchers that lack a persistent, structured representation of the external world. When a system must reason about physics, geometry, multi‑step planning, or long‑term consequences, an LLM alone often falls short. Enter purpose‑built world models—neural or hybrid representations that explicitly encode the state of an environment, simulate dynamics, and allow downstream components to query “what‑if” scenarios. In robotics, autonomous driving, finance, and game AI, world models have already proven indispensable. This guide walks developers through the entire lifecycle of building, deploying, and maintaining such models in production, from conceptual design to real‑time serving. ...