GenAI Retrieval Accelerator Extended Generation (RAG)

What is RAG and Q for Business?

Retrieval Augmented Generation (RAG) is a natural language processing technique that allows generative AI models to refer to an authoritative knowledge base before generating an answer.

RAG can be used to create AI assistants tailored to a specific business domain. Amazon Q for Business enables rapid creation, tuning, and deployment of RAG solutions.

Challenges of building your own RAG:

While tools exist to help organizations build similar capabilities, building them on your own comes with challenges, including:

• Internal expertise in LLM architecture and frameworks needs to be developed.
• You must purchase the appropriate hardware and software.
• It is necessary to ensure continuous maintenance and operational capabilities.
• Security and compliance requirements must be met.
• To ensure the continued accuracy of the assistant, data management, versioning, and re-indexing are necessary.

Who is it for?

Organizations with internal domain-specific knowledge bases; for example, standard operating procedures and market intelligence. Examples of verticals include:

• Financial services such as insurance, banking, investments, capital markets
• Life Sciences and Production
• Energy

How it’s working:

1. Discovery: The RAG Accelerator begins with an initial discovery workshop, typically one day long. The goal of the workshop is to identify high-value business use cases. After the workshop, we determine which of the identified use cases to pilot based on data availability, security and compliance requirements, and end-user needs. We also establish a set of objective business KPIs to measure during the pilot. In total, this phase lasts about a week.
2. Remote: During the pilot phase, we configure and deploy the Q RAG application(s) along with the associated infrastructure required to securely connect to data sources and end users and measure identified KPIs. The pilot typically takes two to three weeks to deploy. We then give users time to work with the assistant to gather feedback.
3. Plan: We measure objective KPIs and, together with subjective user feedback, assess the impact of the pilot on the business. Based on these measurements, we establish an implementation plan to move to full production implementation. The plan typically includes issues such as data security, classification, versioning, and role-based access.
4. Implementation: Once the pilot is complete, we can support the safe and compliant implementation of the RAG solution across the entire organization.

Benefits:

• Increase team productivity: Q for Business streamlines workflows by summarizing documents, generating drafts, conducting research, or performing comparative analyses.
• Significantly reduced code and infrastructure: Compared to a home-built system, implementing Q involves significantly less implementation and maintenance costs, which translates into a lower total cost of ownership (TCO).
• Sustainable development: By using centrally trained and managed models, as opposed to self-training models, Q enables you to reduce your company’s carbon footprint.

Why fourTheorem?

• We wrote a book about it: AI as a Service is a step-by-step guide to deploying cloud-native AI services on AWS.
• AI-powered Risk Assessment Assistant: Our RAG Extended Risk Assessment Assistant has been trained on 24 years of Lloyds of London Stock Exchange Bulletins.