RAG (Retrieval-Augmented Generation) pipeline is a system architecture combining vector search with LLM generation — retrieving relevant documents from a knowledge base and using them as context for accurate, grounded responses.

What Is a RAG Pipeline?

• Definition: Retrieve relevant context, then generate response.
• Components: Embeddings → Vector DB → Retrieval → LLM → Response.
• Purpose: Ground LLM outputs in factual, up-to-date information.
• Benefit: Reduces hallucinations, enables domain-specific knowledge.
• Standard: Used in ChatGPT plugins, enterprise AI, knowledge assistants.

Why RAG Pipelines Matter

• Accuracy: Grounded responses reduce hallucinations.
• Freshness: Access up-to-date information beyond training data.
• Domain Knowledge: Add proprietary documents to LLM knowledge.
• Cost-Effective: Cheaper than fine-tuning for knowledge updates.
• Verifiable: Can cite sources for generated answers.

Pipeline Stages

1. Embed: Convert query to vector. 2. Retrieve: Find top-k similar documents from vector DB. 3. Augment: Add retrieved context to LLM prompt. 4. Generate: LLM produces grounded response.

Key Components

• Embedding model (OpenAI, Cohere, Sentence Transformers).
• Vector database (Pinecone, Qdrant, Milvus, Chroma).
• LLM (GPT-4, Claude, Llama).
• Orchestration (LangChain, LlamaIndex).

RAG is the standard architecture for knowledge-grounded AI — combining retrieval precision with generative fluency.