Case study: Gemini

Definition

Gemini is Google’s family of LLMs with native multimodal support: text, image, audio, and video in one model. It succeeds earlier Google models (e.g. 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, coding, and agent-style tool use.

How it works

Multimodal inputs (text, image, audio, video) are encoded and fused in a unified transformer stack. The decoder generates text (or structured output) conditioned on all modalities. Scale tiers: smaller models (e.g. 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.

Use cases

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 reasoning via API or Google products

External documentation

• Google AI – Gemini — API and overview
• Google – Gemini models — Model tiers and capabilities

See also

• LLMs
• Multimodal AI
• BART — Predecessor in the encoder-decoder line