A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they’re willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.Which model meets all the Generative Al Engineer’s needs in this situation?
A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.Which set of high level tasks should the Generative AI Engineer's system perform?
A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?
Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?
A Generative Al Engineer has successfully ingested unstructured documents and chunked them by document sections. They would like to store the chunks in a Vector Search index. The current format of the dataframe has two columns: (i) original document file name (ii) an array of text chunks for each document.What is the most performant way to store this dataframe?
A Generative Al Engineer is building a system which will answer questions on latest stock news articles.Which will NOT help with ensuring the outputs are relevant to financial news?
