Published: 16 April 2024
Retrieval-augmented generation has become a favored design pattern for improving the quality and accuracy of large language models. Data and analytics technical professionals must understand the key roles and skills their AI teams will need when developing a RAG-based LLM solution.
Technical teams are struggling to develop the right skills to implement the data management, search, and evaluation processes necessary for success with RAG.
The most prominent AI team roles in RAG LLM development are AI architects and the AI engineers. Other roles will be needed too, including data engineers, software engineers, domain experts and security engineers.
There are four phases in a RAG LLM development: Prototype, solution, pilot and production. Successfully implementing a RAG LLM application involves empowering the right team members, with the right skills, to successfully traverse each of these phases.
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