Frameworks for AI applications are libraries and tools that make it easier for developers to build solutions on top of language models. They handle recurring tasks: connecting to various model providers, splitting documents into chunks and retrieving them, assembling context, calling external tools and orchestrating multiple steps in sequence. Thanks to them, a prototype emerges quickly, which is also their risk – there is a long way from a working prototype to reliable operation, and most of the work lies in testing, error handling and monitoring. What matters for a company is that choosing a framework is a technical decision with a long-term impact on maintenance, and that building your own only makes sense once off-the-shelf solutions hit a demonstrable limitation.
See also: Ready-made solution vs custom development, RAG over a company's own content, Retrieval pipeline.
