RAG has emerged as a powerful approach for building advanced AI systems that combine the strengths of large language models with external knowledge sources. However, RAG solutions struggle with reliability and require a lot of experimentation. This session will address key questions to help determine the best design pattern and optimization for RAG implementations.

GenAI solutions include several choices and trade-offs. A critical decision is: should you build custom AI solutions in-house or buy off-the-shelf products? This session brings together a debate on the trade-offs, risk and rewards of each approach. The session will be based on scenarios and use-cases to highlight key considerations such as cost, reliability , flexibility and speed for different decisions such as LLMs vs. SLMs, RAG vs. AI agents, packaged platform capability vs. bespoke custom solution, packaged vs. open-source.

RAG has emerged as a powerful approach for building advanced AI systems that combine the strengths of large language models with external knowledge sources. However, RAG solutions struggle with reliability and require a lot of experimentation. This session will address key questions to help determine the best design pattern and optimization for RAG implementations.

Despite the popularity of retrieval-augmented generation, organizations are struggling to optimize large language model applications based on RAG. Attend this session to get suggestions and recommendations on improving your RAG solution, LLMOps for RAG systems, and scaling considerations.

Despite the popularity of retrieval-augmented generation, organizations are struggling to optimize large language model applications based on RAG. Attend this session to get suggestions and recommendations on improving your RAG solution, LLMOps for RAG systems and scaling considerations.