Retrieval-Augmented Generation (RAG)
An AI architecture combining LLM generation with real-time retrieval from external knowledge sources.
Retrieval-Augmented Generation (RAG) combines large language models with real-time retrieval from external knowledge bases, vector databases, or document stores. Instead of relying solely on training-time knowledge (which becomes stale and cannot reference proprietary data), RAG systems retrieve relevant documents at query time, inject them into the LLM context window, and generate answers grounded in those sources. Production RAG requires careful chunking strategy, embedding model selection, vector database tuning (Pinecone, Weaviate, pgvector), and reranking. Empire325 builds enterprise RAG systems for documentation search, customer support automation, internal knowledge agents, and compliance Q&A applications.
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Large Language Model (LLM)
A neural network trained on massive text corpora to understand and generate human language.
AI Agent
An autonomous LLM-based system that plans, takes actions via tools, and accomplishes multi-step goals.
Fine-Tuning
Adapting a pretrained foundation model to specific tasks or domains via additional training.
Prompt Engineering
The practice of designing inputs to LLMs to elicit accurate, consistent, useful outputs.