Build Production-Ready RAG Systems

If you're building RAG (Retrieval-Augmented Generation) systems but struggling with architecture, retrieval quality, or scaling - this session will help you.

We will discuss:

• Designing production-grade RAG architectures

• Choosing the right vector database (Pinecone / Weaviate / FAISS / Chroma)

• Improving retrieval quality and context relevance

• Handling hallucinations in RAG systems

• Chunking strategies and embedding optimization

• Structuring LLM pipelines for real-world applications

We can also review your existing RAG project and discuss improvements.

Best suited for:

• Developers building GenAI applications

• Engineers working on document AI / knowledge assistants

• Backend engineers integrating LLMs into products

• AI developers facing retrieval or hallucination issues