💻 Architecting Production-Ready Multimodal RAG Pipelines: Building AI That Understands Text, Images & Documents

Large Language Models are no longer limited to understanding text. Today's enterprise knowledge bases contain diagrams, scanned PDFs, screenshots, charts, tables, and images that traditional RAG systems struggle to interpret effectively.

In this hands-on workshop, we'll build a production-inspired Multimodal Retrieval-Augmented Generation (RAG) pipeline capable of retrieving and reasoning across both textual and visual information using modern Vision-Language Models (VLMs).

Participants will learn how multimodal AI systems ingest complex documents, generate unified embeddings, retrieve relevant context across different data types, and produce grounded responses using state-of-the-art LLMs.

Rather than focusing solely on model theory, we'll cover the engineering decisions that matter when deploying these systems in production including document parsing, multimodal embedding strategies, retrieval optimization, hallucination reduction, evaluation frameworks, cloud deployment, and cost-performance tradeoffs.

By the end of the session, attendees will understand how to design scalable AI search applications that can answer questions about technical manuals, research papers, diagrams, charts, screenshots, and image-rich documentation.

Key Takeaways
* Understanding Multimodal RAG architectures
* Vision-Language Models (VLMs) and how they complement LLMs
* Processing PDFs containing text, tables, charts, and images
* Building unified retrieval pipelines across multiple modalities
* Choosing embedding models and vector databases
* Evaluating retrieval quality and reducing hallucinations
* Designing scalable cloud-native architectures for enterprise AI applications
* Production best practices for latency, security, observability, and cost optimization
This workshop is ideal for software engineers, AI engineers, machine learning practitioners, and technical leaders interested in building next-generation enterprise AI systems beyond traditional text-only RAG.