CIR & CIS: Retrieval Augmented Generation (RAG) Pipelines

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Presentation: Retrieval Augmented Generation (RAG) Pipelines
 
Abstract: Large language models provide powerful capabilities for generating natural language responses, but systems built solely on generative models often suffer from hallucinations, outdated knowledge, and limited domain accuracy. Retrieval Augmented Generation (RAG) addresses these challenges by combining modern information retrieval techniques with generative AI models. In a RAG system, relevant documents are retrieved from external knowledge sources at query time and provided to the model as contextual input, enabling responses grounded in verifiable information. This talk explains the architecture of RAG pipelines and walks through the stages involved in building them, including data collection, document processing, embedding generation, vector search, retrieval, and prompt construction. Implementation approaches and tooling in both the .NET and Python ecosystems will be discussed, along with considerations for data curation, web scraping, and responsible engineering practices when building AI systems. Attendees gain practical skills to build accurate, production-ready AI systems using Retrieval Augmented Generation and real-world tools.

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  • Starts 05 May 2026 06:00 AM UTC
  • Ends 21 May 2026 12:00 AM UTC
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Scott Swindell

Topic:

Retrieval Augmented Generation (RAG) Pipelines

Abstract: Large language models provide powerful capabilities for generating natural language responses, but systems built solely on generative models often suffer from hallucinations, outdated knowledge, and limited domain accuracy. Retrieval Augmented Generation (RAG) addresses these challenges by combining modern information retrieval techniques with generative AI models. In a RAG system, relevant documents are retrieved from external knowledge sources at query time and provided to the model as contextual input, enabling responses grounded in verifiable information. This talk explains the architecture of RAG pipelines and walks through the stages involved in building them, including data collection, document processing, embedding generation, vector search, retrieval, and prompt construction. Implementation approaches and tooling in both the .NET and Python ecosystems will be discussed, along with considerations for data curation, web scraping, and responsible engineering practices when building AI systems. Attendees gain practical skills to build accurate, production-ready AI systems using Retrieval Augmented Generation and real-world tools.

Biography:

Mr. Scott J. Swindell is a software engineering leader, architect, and entrepreneur with more than twenty-six years of professional experience building complex software systems across healthcare, enterprise platforms, financial systems, and emerging AI-enabled applications. Mr. Swindell began programming at age seven on an Apple IIe and has spent decades designing software, mentoring engineers, and advancing practical software engineering practices. Mr.Swindell is the Founder and CEO of Teos Technology LLC, where he leads development of AI‑native platforms and modern cloud applications. Mr. Swindell also consulted for the U.S. Department of Veterans Affairs within the Veterans Health Administration, serving as a senior developer, architect, and team lead on mission‑critical healthcare systems. Mr.Swindell earned an M.S. in Computer Science from the University of Oklahoma, where he focused on developing software control systems for a prototype Mars rover platform.

Address:Colorado, United States