Design and implementation of Retrieval-Augmented Generation systems that connect language models to external documents and knowledge sources.
The work can cover document ingestion, chunking, embeddings, vector search, retrieval strategies, context construction, and response generation.
RAG systems are useful for technical documentation, research collections, organizational knowledge, scientific literature, and domain-specific information services.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.