OGGlossary OSVocabulario de Solana reconstruido para builders.
ENPT-BRES
IA / 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
Lectura rápida

Lectura rápida

Empieza por la explicación más corta y útil antes de profundizar.

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.

Modelo mental

Modelo mental

Usa primero la analogía corta para razonar mejor sobre el término cuando aparezca en código, docs o prompts.

Piensa en esto como una pieza de la pila de contexto o inferencia usada en productos con agentes o LLMs.

Contexto técnico

Contexto técnico

Ubica el término dentro de la capa de Solana en la que vive para razonar mejor sobre él.

LLMs, RAG, embeddings, inferencia y primitivas orientadas a agentes.

Por qué le importa a un builder

Por qué le importa a un builder

Convierte el término de vocabulario en algo operacional para producto e ingeniería.

Este término desbloquea conceptos adyacentes rápido, así que funciona mejor cuando lo tratas como un punto de conexión y no como una definición aislada.

Handoff para IA

Handoff para IA

Usa este bloque compacto cuando quieras dar contexto sólido a un agente o asistente sin volcar toda la página.

Chain-of-Thought (CoT) (chain-of-thought)
Categoría: IA / ML
Definición: 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
Relacionados: Reasoning Model, Ingeniería de Prompt, Structured Output
Glossary Copilot

Haz preguntas de Solana con contexto aterrizado sin salir del glosario.

Usa contexto del glosario, relaciones entre términos, modelos mentales y builder paths para recibir respuestas estructuradas en vez de output genérico.

Abrir workspace completa del Copilot
Explicar este código

Opcional: pega código Anchor, Solana o Rust para que el Copilot mapee primitivas de vuelta al glosario.

Haz una pregunta aterrizada en el glosario

Haz una pregunta aterrizada en el glosario

El Copilot responderá usando el término actual, conceptos relacionados, modelos mentales y el grafo alrededor del glosario.

Grafo conceptual

Ve el término como parte de una red, no como una definición aislada.

Estas ramas muestran qué conceptos toca este término directamente y qué existe una capa más allá de ellos.

Rama

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 (Modelo de Lenguaje Grande)Inference
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Rama

Ingeniería de Prompt

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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Rama

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)
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Siguientes conceptos para explorar

Mantén la cadena de aprendizaje en movimiento en lugar de parar en una sola definición.

Estos son los siguientes conceptos que vale la pena abrir si quieres que este término tenga más sentido dentro de un workflow real de Solana.

IA / 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.

Abrir término
IA / ML

Ingeniería de Prompt

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.

Abrir término
IA / 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.

Abrir término
IA / 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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Comúnmente confundido con

Términos cercanos en vocabulario, acrónimo o vecindad conceptual.

Estas entradas son fáciles de mezclar cuando lees rápido, haces prompting a un LLM o estás entrando en una nueva capa de Solana.

IA / 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.

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IA / 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.

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Términos relacionados

Sigue los conceptos que realmente le dan contexto a este término.

Las entradas del glosario se vuelven útiles cuando están conectadas. Estos enlaces son el camino más corto hacia ideas adyacentes.

IA / 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.

Abrir término
IA / MLprompt-engineering

Ingeniería de Prompt

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.

Abrir término
IA / 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.

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Más en la categoría

Quédate en la misma capa y sigue construyendo contexto.

Estas entradas viven junto al término actual y ayudan a que la página se sienta parte de un grafo de conocimiento más amplio en lugar de un callejón sin salida.

IA / ML

LLM (Modelo de Lenguaje Grande)

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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IA / 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).

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IA / 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.

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IA / 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.

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