An AI architecture that combines LLMs with external knowledge retrieval. Instead of relying solely on training data, RAG systems retrieve relevant documents from a knowledge base (using embeddings and vector search), then provide them as context to the LLM. RAG reduces hallucinations and enables up-to-date responses. Useful for blockchain documentation bots and developer assistants.
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An AI architecture that combines LLMs with external knowledge retrieval. Instead of relying solely on training data, RAG systems retrieve relevant documents from a knowledge base (using embeddings and vector search), then provide them as context to the LLM. RAG reduces hallucinations and enables up-to-date responses. Useful for blockchain documentation bots and developer assistants.
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LLMs, RAG, embeddings, inference, and agent-facing primitives.
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RAG (Retrieval-Augmented Generation) (rag) Category: AI / ML Definition: An AI architecture that combines LLMs with external knowledge retrieval. Instead of relying solely on training data, RAG systems retrieve relevant documents from a knowledge base (using embeddings and vector search), then provide them as context to the LLM. RAG reduces hallucinations and enables up-to-date responses. Useful for blockchain documentation bots and developer assistants. Aliases: RAG Related: Embedding, Vector Database
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A dense vector representation of text (or other data) in a continuous high-dimensional space where semantically similar items are closer together. Embedding models (OpenAI ada-002, Cohere, sentence-transformers) convert text to vectors of 256-3072 dimensions. Used in RAG for semantic search, in recommendation systems, and for clustering. Stored and queried via vector databases.
A database optimized for storing and querying high-dimensional vector embeddings using similarity search (cosine distance, dot product, Euclidean distance). Examples: Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Vector databases power RAG systems by quickly finding the most relevant documents for a given query embedding. Essential for AI-powered developer tools and documentation search.
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A dense vector representation of text (or other data) in a continuous high-dimensional space where semantically similar items are closer together. Embedding models (OpenAI ada-002, Cohere, sentence-transformers) convert text to vectors of 256-3072 dimensions. Used in RAG for semantic search, in recommendation systems, and for clustering. Stored and queried via vector databases.
A database optimized for storing and querying high-dimensional vector embeddings using similarity search (cosine distance, dot product, Euclidean distance). Examples: Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Vector databases power RAG systems by quickly finding the most relevant documents for a given query embedding. Essential for AI-powered developer tools and documentation search.
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A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).
A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.
A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.