What is an AI Stack?

An AI stack, specifically a generative AI (gen AI) stack, refers to a comprehensive combination of tools, libraries, and solutions leveraged to create applications with generative AI capabilities. The components of the AI stack include programming languages, model providers, LLM frameworks, vector databases, operational databases, monitoring and evaluation tools, and deployment solutions.

The choice impacts your project's development process, scalability, ethical considerations, and commercial flexibility. Factors like resource availability, project requirements, cost, and privacy considerations will guide whether open source or closed-source models are more suitable for your needs. Open source models offer transparency and community collaboration, while closed-source models provide streamlined access to powerful AI capabilities through APIs but with more restricted control.

Programming languages are crucial in determining the selection of stack components and the overall architecture of the application developed. Python, JavaScript, and TypeScript are prominent choices due to their extensive support for AI and data science libraries, as well as their flexibility and readability.

LLM frameworks, like LlamaIndex and LangChain, abstract complex development processes involved in creating LLM-powered AI applications. They facilitate connections between vector databases and LLMs, implement prompt engineering techniques, and manage data indexing and ingestion, reducing the need for extensive coding.

MongoDB is a modern, multi-cloud database that manages and stores vector and operational data. It offers robust data management and search capabilities. MongoDB supports the real-time processing needs of modern AI applications, handles unstructured data efficiently, and integrates well with LLM orchestrators and frameworks.