Case study: Gemini
Definición
Gemini is Google’s familia de LLMs with native multimodal support: texto, imagen, audio y video en un solo modelo. It succeeds earlier Google models (por ej. BART in the encoder-decoder line) and is offered in multiple scale tiers (Nano, Pro, Ultra) for different latency and capability trade-offs.
Gemini is trained and deployed across Google products (Search, Workspace, Vertex AI, Android). Use case: chat, multimodal understanding and generation, codificación, and agent-style tool use.
Cómo funciona
Las entradas multimodales (texto, imagen, audio, video) se codifican y fusionan en un transformer unificadotack. The decoder generates text (or structured output) conditioned on all modalities. Scale tiers: modelo más pequeños (por ej. Nano) for edge and on-device; larger (Pro, Ultra) for maximum capability in the cloud. Integration: same models power Gemini in Search, Workspace, and Vertex AI APIs. Prompt engineering and RAG or tools extend use in applications.
Casos de uso
Gemini fits when you need multimodal understanding or generation and optional integration with Google’s stack.
- Chat and assistants with image, document, or video understanding
- Multimodal search, summarization, and content generation
- Coding and razonamiento via API or Google products
Documentación externa
- Google AI – Gemini — API and overview
- Google – Gemini models — Model tiers and capabilities
Ver también
- LLMs
- Multimodal AI
- BART — Predecessor in the encoder-decoder line