Glossary OS

Lectura rápida

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

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.

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

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

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

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

Structured Output (structured-output)
Categoría: IA / ML
Definición: 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
Relacionados: Tool Use (Function Calling), LLM (Modelo de Lenguaje Grande), Chain-of-Thought (CoT)

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

Pregunta

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

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

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.

Agente de IA

Abrir término

Rama

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.

Transformer Foundation Model

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Rama

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 Ingeniería de Prompt

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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

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.

Abrir término

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.

Abrir término

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.

Abrir término

IA / 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.

Abrir término

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 / 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.

Abrir término

IA / MLllm

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.

Abrir término

IA / 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.

Abrir término

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.

Abrir término

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).

Abrir término

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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