# LLM Workflow ## Fine-Tuning and Serving LLMs This diagram shows the workflow for fine-tuning and serving large language models (LLMs).

Some of the steps in the workflow are optional. For example, you can directly serve a pretrained LLM without fine-tuning. ## Deploying Vector Stores and AI Agents This diagram shows the workflow for serving LLMs or embeddings models for deploying vector stores for RAG applications or deploying AI agents.

Workflow for Vector Stores and AI Agents

Some of the steps in the workflow are optional. For example, you do not need to serve LLMs if the LLMs that you plan to use are from a non-Aizen vendor, such as OpenAI. Also, you are only required to configure a vector store if you plan to add a RAG Query tool to the AI agent. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://aizen-corp.gitbook.io/docs/managing-llm-workflows/llm-workflow.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.