Gemini

Gemini is the language-model layer that reads retrieved context and produces a response. Embedding and vector retrieval are separate components; Gemini is not the knowledge base itself.

Output depends on the question, retrieved passages, system instructions, and selected model. Each layer solves a different problem, so the model name should not be treated as the whole RAG system.

The central types package defines Gemini 3.7 Flash plus lite and pro variants for the application stack. If a default changes, content, cost accounting, and operational expectations should be reviewed together.

Kobi Kredi relates Gemini input and output tokens to model pricing. An estimate and completed-turn usage can differ because token counts and calculation rules are not identical.

Only context the user is allowed to access and that is relevant to the question should reach Gemini. Company, department, and role boundaries belong in retrieval and cannot be replaced by general model knowledge.

Check the source passage for legal, finance, and HR answers. Fluent model output is not decision evidence; an authorised team should approve the current policy when needed.

A useful way to evaluate gemini is to follow one real question from the source document to the final answer. Record which file was selected, what context reached the model, and what a reviewer would need to verify. This turns a definition into an operational check and makes the result comparable across teams.

The same check should include ownership and change management. Decide who updates the relevant documents, how an outdated result is reported, and which access boundary applies. KobiGPT can provide the assistant and the cited document context, but the organisation still owns the source material, permissions, and the decision made from the answer.