An LLM capability where the model generates structured calls to external tools/functions rather than just text. The model decides which tool to invoke and with what parameters. Examples: calling an API, executing code, querying a database, or reading a file. Tool use enables agents to interact with the real world. Claude, GPT-4, and Gemini support native tool use.
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An LLM capability where the model generates structured calls to external tools/functions rather than just text. The model decides which tool to invoke and with what parameters. Examples: calling an API, executing code, querying a database, or reading a file. Tool use enables agents to interact with the real world. Claude, GPT-4, and Gemini support native tool use.
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Tool Use (Function Calling) (tool-use) Category: AI / ML Definition: An LLM capability where the model generates structured calls to external tools/functions rather than just text. The model decides which tool to invoke and with what parameters. Examples: calling an API, executing code, querying a database, or reading a file. Tool use enables agents to interact with the real world. Claude, GPT-4, and Gemini support native tool use. Aliases: Function Calling Related: AI Agent, LLM (Large Language Model)
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An autonomous AI system that can plan, use tools, and take actions to accomplish goals. Agents use LLMs as the reasoning core and have access to tools (APIs, code execution, web browsing, database queries). In blockchain: agents can analyze smart contracts, execute transactions, monitor DeFi positions, and automate trading strategies. Frameworks: LangChain, CrewAI, Claude Agent SDK.
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.
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An autonomous AI system that can plan, use tools, and take actions to accomplish goals. Agents use LLMs as the reasoning core and have access to tools (APIs, code execution, web browsing, database queries). In blockchain: agents can analyze smart contracts, execute transactions, monitor DeFi positions, and automate trading strategies. Frameworks: LangChain, CrewAI, Claude Agent SDK.
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.
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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.