Retrieval-Augmented Generation (RAG)
AI / MCPDefinition
RAG is an AI architecture that combines information retrieval with text generation. It first retrieves relevant documents from external sources, then uses them as context for the language model to generate accurate, grounded responses.
How It Relates to CrawlForge
RAG systems need high-quality source content to work well. Garbage in means garbage out -- if the retrieved documents are noisy HTML with navigation menus and ads, the generated answers suffer. Clean content extraction is a critical component of any RAG pipeline.
CrawlForge tools like extract_content and extract_text return clean, structured content stripped of boilerplate. This makes them ideal for building RAG pipelines that need to ingest web content. Pair them with deep_research for multi-source retrieval with built-in conflict detection.
Related CrawlForge Tools
Related Terms
Embeddings
Embeddings are dense numerical vector representations of text, images, or other data. They capture semantic meaning in a format that enables similarity search, clustering, and other machine learning operations.
Vector Database
A vector database is a specialized database designed to store and efficiently query high-dimensional vector embeddings. It enables fast similarity search across millions of embedded documents.
Large Language Model (LLM)
A large language model is a neural network trained on vast amounts of text data that can understand and generate human language. LLMs power AI assistants, code generators, and autonomous agents.
Context Window
The context window is the maximum amount of text (measured in tokens) that a language model can process in a single request. It includes both the input prompt and the generated output.
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