Map the glossary to the agentic workflow and context retrieval stack.
The path is sequenced to reduce context switching. Start at the top, open each term, and use related links when a concept needs more depth.
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.
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.
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.
The JSON-RPC 2.0 protocol interface through which clients communicate with Solana nodes to query chain state, submit transactions, and subscribe to events. Solana exposes a rich set of HTTP and WebSocket endpoints (e.g., getAccountInfo, sendTransaction) that abstract direct peer-to-peer network participation, making RPC the primary integration point for wallets, dApps, and indexers.
A Solana MEV infrastructure project by Jito Labs that operates a modified validator client (jito-solana), a Block Engine for bundle simulation and routing, and a tip distribution program that collects and distributes MEV tips to staked validators. Jito's client is run by a supermajority of Solana's stake-weighted validators, making its bundle and tip market the de-facto MEV layer for the network. Jito also operates a restaking and liquid staking protocol (JitoSOL) using proceeds from MEV tip redistribution.
The path is sequenced to reduce context switching. Start at the top, open each term, and use related links when a concept needs more depth.
Read the path in order instead of jumping randomly between terms.
Open term pages whenever a concept needs definitions, aliases, and related links.
Use related terms and category continuation to go deeper without losing the path context.
These entries live beside the current term and help the page feel like part of a larger knowledge graph instead of a dead end.
Learn the trading and liquidity vocabulary behind the app layer.