How to deploy LLM apps
Given the increasing use of LLMs in various sectors, it’s imperative to understand how to effectively deploy models and apps into production. Deployment services and frameworks can help to scale the technical hurdles. There are lots of different ways to productionize LLM apps or applications with generative AI.
Deployment for production requires research into, and knowledge of, the generative AI ecosystem, which encompasses different aspects including:
- Models and LLM-as-a-Service: LLMs and other models either run on-premises or offered as an API on vendor-provided infrastructure.
- Reasoning heuristics: Retrieval Augmented Generation (RAG), Tree-of-Thought, and others.
- Vector databases: Aid in retrieving contextually relevant information for prompts.
- Prompt engineering tools: These facilitate in-context learning without requiring expensive fine-tuning or sensitive data.
- Pre-training and fine-tuning: For models specialized...
