Retail Demand Forecasting Using RAG, LLMs and Agentic AI

A retail demand forecasting system that uses RAG, LLMs and agentic AI.

A retail demand forecasting system that uses RAG, LLMs and agentic AI. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are retrieval over an indexed document collection that grounds the language model's answers, cooperating agents with defined roles and tools, text or speech data preparation, model training, fine-tuning or prompting, and evaluation with task-appropriate metrics. The reference build uses LangChain, FAISS, LLM APIs, Python, and Hugging Face Transformers, with a clear project structure, setup instructions and sample data so that it can be run and extended.