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An LLM capability that constrains model output to conform to a predefined schema, typically JSON or XML, enabling reliable programmatic consumption of responses. Supported natively by OpenAI, Anthropic, and others via API parameters that enforce valid JSON matching a JSON Schema. Research shows strict format constraints can degrade reasoning quality, so a best practice is to include a 'reasoning' field before answer fields in the schema.

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Structured Output (structured-output)
Category: AI / ML
Definition: An LLM capability that constrains model output to conform to a predefined schema, typically JSON or XML, enabling reliable programmatic consumption of responses. Supported natively by OpenAI, Anthropic, and others via API parameters that enforce valid JSON matching a JSON Schema. Research shows strict format constraints can degrade reasoning quality, so a best practice is to include a 'reasoning' field before answer fields in the schema.
Aliases: JSON Mode, Constrained Decoding
Related: Tool Use (Function Calling), LLM (Large Language Model), Chain-of-Thought (CoT)

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Tool Use (Function Calling)

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.

AI Agent

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LLM (Large Language Model)

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.

Transformer Foundation Model

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Chain-of-Thought (CoT)

A prompting technique or model-native capability where the LLM produces intermediate reasoning steps before arriving at a final answer, improving accuracy on multi-step problems. Originally a prompting strategy ('think step by step'), CoT is now built directly into reasoning models like o1 and DeepSeek-R1 as an internal process. When combining CoT with structured output, developers should place reasoning fields before answer fields to avoid bypassing the reasoning process.

Reasoning Model Prompt Engineering

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AI / ML

Tool Use (Function Calling)

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.

Open term

AI / ML

LLM (Large Language Model)

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.

Open term

AI / ML

Chain-of-Thought (CoT)

A prompting technique or model-native capability where the LLM produces intermediate reasoning steps before arriving at a final answer, improving accuracy on multi-step problems. Originally a prompting strategy ('think step by step'), CoT is now built directly into reasoning models like o1 and DeepSeek-R1 as an internal process. When combining CoT with structured output, developers should place reasoning fields before answer fields to avoid bypassing the reasoning process.

Open term

AI / ML

Synthetic Data (AI Training)

Artificially generated training data produced by LLMs or other AI models, used to augment or replace human-annotated datasets. Techniques include prompt-based generation, retrieval-augmented pipelines, and iterative self-refinement. Synthetic data slashes costs from $5-20 per human preference point to under $0.01 per sample and became central to post-training pipelines in 2024-2025.

Open term

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AI / MLtool-use

Tool Use (Function Calling)

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.

Open term

AI / MLllm

LLM (Large Language Model)

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.

Open term

AI / MLchain-of-thought

Chain-of-Thought (CoT)

A prompting technique or model-native capability where the LLM produces intermediate reasoning steps before arriving at a final answer, improving accuracy on multi-step problems. Originally a prompting strategy ('think step by step'), CoT is now built directly into reasoning models like o1 and DeepSeek-R1 as an internal process. When combining CoT with structured output, developers should place reasoning fields before answer fields to avoid bypassing the reasoning process.

Open term

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AI / ML

LLM (Large Language Model)

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.

Open term

AI / ML

Transformer

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).

Open term

AI / ML

Attention Mechanism

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.

Open term

AI / ML

Foundation Model

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.

Open term