Building Scalable AI Agents at a Global Finance Company
When our financial services client decided to scale their AI solutions, we delivered immersive, practical training to their engineering teams in developing and implementing multi-agent systems.
Success Stories
Building Production-Grade Retrieval Augmented Generation (RAG) Systems
We developed an interactive learning environment to train engineering teams at a global organisation in RAG systems, ensuring comprehensive technical understanding.
Client
Financial Service Corporation
Industry
Financial Services
Specialism
AI
The challenge
Our global financial services client is implementing multiple engineering solutions that leverage custom Retrieval-Augmented Generation components. They faced challenges in ensuring a common best-practice approach to RAG architecture and implementation was maintained across their global engineering population.
Our solution
We developed a highly practical programme to take experienced engineering teams through the concepts, architecture and implementations of RAG systems.
Participants developed:
• An expert understanding of the architecture of retrieval augmented generation systems, vector embeddings, hybrid query structure and more.
• Real-world practical experience with implementing complex RAG systems using large datasets across multiple data stores.
• Best practices for querying vectors stores, chunking and vector retrieval with LLM prompts.
Delivery
Learners participated in a highly interactive learning environment, during which they were tasked with architecting production-grade RAG systems using their specific tech-stack and integrations.
Key Results
Teams developed expert capabilities in architecting and implementing RAG systems
Participants gained hands-on experience with building and optimising applications based on RAG
Programme outcomes
Our programme successfully upskilled our client’s engineering teams, providing a detailed understanding of production-grade patterns for architecting and building Retrieval-Augmented Generation (RAG) systems. This enabled the organisation to further its goal of creating state-of-the-art AI solutions.
Get in touch
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