Large language models (LLMs) have proven themselves as powerful tools, but by themselves, they’re often less reliable than they appear. The term “stochastic parrots” aptly describes their tendency to generate output that can be inaccurate or nonsensical. When combined with data for retrieval-augmented generation (RAG), however, LLMs become far more reliable. RAG systems can pull in relevant data, minimizing errors like “hallucinations” where LLMs invent information. Connecting these systems to software that can perform tasks—like sending emails or interacting with other applications—creates what are known as AI agents, making them far more practical and useful. But these systems don’t just appear fully formed; they require a framework to integrate and orchestrate the various components.
LLM application frameworks serve as the essential infrastructure, or “plumbing,” that ties these components together. Think of them as orchestration providers that help streamline how LLMs interact with data sources, vector databases, and other software. In a RAG application, for example, the framework is responsible for linking encoders to vector databases, enhancing user queries with the results from database lookups, and passing that information along to the LLM. The model’s output is then sent back to the user. Frameworks like Haystack utilize components and pipelines to create and manage these complex interactions, making it easier to build and deploy applications using LLMs.
The primary benefit of LLM application frameworks is that they significantly reduce the amount of code developers need to write. These frameworks have been designed and refined by experts, thoroughly tested by a wide user base, and proven in production environments. This gives developers confidence that the “plumbing” will function correctly, allowing them to focus on higher-level tasks rather than coding all the underlying infrastructure from scratch.
LLM application frameworks have diverse use cases across a range of industries and applications. They can be used in systems for RAG, chatbot development, AI agents, generative multi-modal question answering, information extraction from documents, and more. While all of these applications rely on LLMs, vector search, and data retrieval, each serves a different purpose, from automating simple tasks to answering complex questions or analyzing large amounts of text. The frameworks make it easier to build these specialized applications, ensuring they function reliably and efficiently.