LLM Orchestration
The framework (e.g., LangChain, semantic routing) coordinating calls, prompts, and tools for an LLM.
LLM Orchestration is the software framework that manages the flow of data between large language models and external application components. It coordinates prompt engineering, memory management, tool access, and model API calls to build complex AI applications.
How it Works
Orchestration layers act as the glue between LLMs and external resources.
- Prompt Construction: Compiles user input, system templates, user persona context, and retrieved document chunks into formatted prompts.
- Workflow Control: Implements logic chains (such as sequential tasks or conditional routing) based on model outputs, directing output to specific APIs.
- State Management: Persists conversation history and variables across multiple inference cycles, managing context window allocation.
Lakehouse & Agentic Relevance
In a data lakehouse, analytical workflows require access to schemas and storage targets. LLM orchestration coordinates how an agent retrieves metadata from catalogs and executes SQL queries. Dremio integrates with orchestration frameworks (such as LangChain or LlamaIndex) by serving as a unified database connector. Dremio provides rapid access to structured data, allowing the orchestration layer to fetch grounding tables and execute SQL without manual catalog mappings.