Building Reliable RAG (Retrieval-Augmented Generation) Solutions

The problem

Many teams understand what RAG is conceptually, but struggle to design a solution that consistently retrieves relevant information, handles poor-quality data, avoids hallucinations, and scales beyond a simple proof of concept. The challenge is often not the model itself, but the retrieval architecture, data quality, chunking strategy, and evaluation approach.

The solution

Learn a structured approach to designing RAG pipelines, including retrieval strategies, data preparation, indexing, evaluation techniques, and common failure modes. The session focuses on practical experience and real-world implementation considerations rather than theoretical concepts.

What you'll walk away with

• Understanding of how modern RAG architectures work
• Knowledge of the most common implementation pitfalls
• A framework for assessing whether RAG fits your use case
• Recommendations for next steps tailored to your scenario