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RAG & Vector Database Training for AI Developers in Akron

Master Retrieval-Augmented Generation (RAG) by building complete file-based AI systems using embeddings and vector databases. This instructor-led course goes beyond basic prompt engineering to teach you how to connect LLMs with enterprise documents, design intelligent search pipelines, and deploy real-world GenAI applications through hands-on, step-by-step training.

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Build Real-World RAG Pipelines: Develop end-to-end systems from scratch, moving from document ingestion to live deployment.
Master Embeddings & Vector Search: Learn to convert text into numerical representations and implement high-performance similarity search.
Enterprise File-Based AI: Gain hands-on experience building chatbots that securely interact with PDF, DOCX, and TXT files.
Enterprise training for teams:
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As Generative AI shifts from simple chat interfaces to complex business tools, the demand for RAG Engineers and Vector Database Specialists has surged. This course at NevoLearn bridges the gap between prompt engineering and AI development. By mastering NLP-to-SQL workflows, metadata filtering, and retrieval-relevance scoring, you gain the skills necessary to build AI that is accurate, grounded, and secure. Our instructor-led sessions provide the practical "why" behind embedding dimensions and indexing strategies, ensuring you can deploy production-ready GenAI applications that meet modern corporate standards.

This intensive 60-hour certification program provides deep-dive training into Retrieval-Augmented Generation (RAG) architecture. Unlike basic AI courses, this curriculum focuses on the mechanical integration of LLMs with private enterprise data. Key technical modules include high-performance chunking strategies for PDFs and DOCX files, embedding model selection, and similarity search optimization within vector databases. Students will work through hands-on labs to implement streaming responses, handle token limits, and manage AI hallucinations. The course culminates in an enterprise-grade capstone project: building a fully functional, file-based AI chatbot capable of secure, real-time data retrieval.