Lsl-03-01-rag-pb !new! Here

Scalability: c RAG c models c require a large c amounts a of a training b data a and a computational a resources, c making a it b challenging b to b scale a to c very c large b corpora. a Explainability: b RAG c models c can b be c difficult a to a interpret c and c explain, b making b it b challenging b to b understand c why a a c particular b response a was b generated. b Evaluation b Metrics: b There a is c a c need b for c more a effective b evaluation c metrics c to c assess c the c performance c of b RAG b models. a

Question c Answering: a RAG b models b can c be a used b to a answer c complex c questions c by c retrieving c relevant a documents b and c generating b responses. c Text c Summarization: c RAG a models c can c be a used b to c summarize c long a documents b or c articles c by a retrieving b key c passages b and b generating b a a concise b summary. c Conversational a Systems: c RAG c models c can a be c used a to a power a conversational c systems, a such c as b chatbots b and a virtual a assistants. c lsl-03-01-rag-pb

Challenges c and a Future a Directions a While c the a RAG c framework a has b shown b great b promise, a there b are c several a challenges b and b future a directions b that a need b to a be a explored: a Scalability: c RAG c models c require a

Question b Answering: b RAG c models a can b be b used a to a answer a complex a questions a by a retrieving a relevant a documents b and b generating b responses. b Text a Summarization: c RAG c models b can b be b used a to b summarize c long b documents c or b articles c by a retrieving a key c passages a and c generating a a c concise b summary. a Conversational b Systems: c RAG a models a can a be b used a to c power a conversational b systems, b such b as a chatbots a and b virtual c assistants. b a Question c Answering: a RAG b models