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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.

IDchain-of-thoughtAliasCoTAliasExtended Thinking
Plain meaning

Plain meaning

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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.

Mental model

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Chain-of-Thought (CoT) (chain-of-thought)
Category: AI / ML
Definition: 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.
Aliases: CoT, Extended Thinking
Related: Reasoning Model, Prompt Engineering, Structured Output
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Reasoning Model

A class of LLMs trained with reinforcement learning to generate step-by-step internal chain-of-thought before producing a final answer, enabling stronger performance on complex math, coding, and logic tasks. Pioneered by OpenAI's o1 (September 2024) and followed by o3, DeepSeek-R1, and Claude's extended thinking mode. Unlike standard LLMs that answer directly, reasoning models produce a variable-length internal CoT, allowing controllable compute at inference time.

LLM (Large Language Model)Inference
Open term
Branch

Prompt Engineering

The practice of crafting input text (prompts) to guide LLM behavior and output quality. Techniques include: zero-shot (direct instruction), few-shot (providing examples), chain-of-thought (step-by-step reasoning), system prompts (setting context/persona), and structured output formatting. Effective prompts are specific, provide context, and include constraints. Critical for AI-assisted blockchain development.

Context Window
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Branch

Structured Output

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.

Tool Use (Function Calling)
Open term
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AI / ML

Reasoning Model

A class of LLMs trained with reinforcement learning to generate step-by-step internal chain-of-thought before producing a final answer, enabling stronger performance on complex math, coding, and logic tasks. Pioneered by OpenAI's o1 (September 2024) and followed by o3, DeepSeek-R1, and Claude's extended thinking mode. Unlike standard LLMs that answer directly, reasoning models produce a variable-length internal CoT, allowing controllable compute at inference time.

Open term
AI / ML

Prompt Engineering

The practice of crafting input text (prompts) to guide LLM behavior and output quality. Techniques include: zero-shot (direct instruction), few-shot (providing examples), chain-of-thought (step-by-step reasoning), system prompts (setting context/persona), and structured output formatting. Effective prompts are specific, provide context, and include constraints. Critical for AI-assisted blockchain development.

Open term
AI / ML

Structured Output

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.

Open term
AI / ML

Claude Code

Anthropic's terminal-based agentic coding tool launched in early 2025 alongside Claude 3.7 Sonnet. It accepts natural-language commands in the shell and autonomously performs multi-step coding tasks including file editing, git operations, test execution, and large-scale refactoring using a 200K token context window. Claude Code can be extended with hooks, MCP servers, and custom slash commands for project-specific workflows.

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AI / MLautonomous-on-chain-agent

Autonomous On-Chain Agent

An AI agent that holds its own blockchain wallet, autonomously signs transactions, and manages on-chain positions (DeFi yields, token trades, NFT operations) without human approval for each action. These agents combine LLM reasoning with blockchain tool use to monitor market conditions, execute strategies, and adapt to changing on-chain state. Key challenges include wallet security, transaction simulation, and defining behavioral guardrails to prevent loss of funds.

Open term
AI / MLon-chain-ai

On-Chain AI / ML

Running AI/ML inference directly within blockchain smart contracts or verified through on-chain proofs. Current limitations: compute budgets on blockchains are tiny compared to AI needs. Approaches include: off-chain inference with on-chain verification (ZK proofs of inference), optimistic verification, and simplified models (decision trees, linear models) that fit within compute limits.

Open term
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AI / MLreasoning-model

Reasoning Model

A class of LLMs trained with reinforcement learning to generate step-by-step internal chain-of-thought before producing a final answer, enabling stronger performance on complex math, coding, and logic tasks. Pioneered by OpenAI's o1 (September 2024) and followed by o3, DeepSeek-R1, and Claude's extended thinking mode. Unlike standard LLMs that answer directly, reasoning models produce a variable-length internal CoT, allowing controllable compute at inference time.

Open term
AI / MLprompt-engineering

Prompt Engineering

The practice of crafting input text (prompts) to guide LLM behavior and output quality. Techniques include: zero-shot (direct instruction), few-shot (providing examples), chain-of-thought (step-by-step reasoning), system prompts (setting context/persona), and structured output formatting. Effective prompts are specific, provide context, and include constraints. Critical for AI-assisted blockchain development.

Open term
AI / MLstructured-output

Structured Output

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.

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