Recap—RAG Time: How Experts are Harnessing Retrieval-Augmented Generation

The September Sandbox Working Group of the Natural Language Processing Community of Practice webinar on Retrieval Augmented Generation (RAG) brought together three experts to share their experiences and insights on this cutting-edge AI technology in evaluation applications. NLP CoP members Guillaume Soto, Maria Dyshel, and James Goh offered diverse perspectives on the applications, challenges, and future of RAG systems.

(This blog post was 95% generated by Claude using RAG to review the webinar transcript—freeing up more of our time to work on organizing more events for the community.)

View the webinar recording here.

Key Themes and Insights:

1. The Power and Limitations of RAG: RAG systems shine when dealing with large volumes of proprietary or specialized data that aren’t part of an AI model’s training set. Guillaume Soto highlighted how RAG enabled the processing of 3,000 documents for synthesis, while Maria Dyshel emphasized its potential for making organizational knowledge more accessible through her company’s platform, Delvin. However, all speakers noted that current RAG systems typically achieve accuracy rates around 60-70%, underscoring the need for careful implementation and user expectation management.
2. Evaluation and Improvement: A significant portion of the discussion focused on the importance of rigorous evaluation frameworks for RAG systems. Guillaume Soto shared his experience using the Giskard framework to generate test questions and evaluate system performance. Maria Dyshel advocated for a multi-faceted approach, combining different evaluation toolkits such as Ragas, DeepEval by Confident AI, and TruLens to get a comprehensive view of system performance.
3. The Complexity of Implementation: While RAG offers powerful capabilities, the speakers emphasized that implementing an effective system requires significant expertise and customization. Maria Dyshel mentioned using popular frameworks like LangChain and LlamaIndex for building RAG pipelines. From document ingestion challenges to prompt engineering and retrieval optimization, RAG systems demand careful tuning to achieve optimal results.
4. Future Trends and Alternatives: James Goh, creator of AIlyze, offered a thought-provoking perspective on potential alternatives to RAG, highlighting trends such as exponentially increasing context windows in language models like GPT-3.5 and Gemini Pro, rapidly decreasing processing costs, and emerging paradigms like AI agents and continuous model fine-tuning (referencing Apple’s Lora approach). These developments may reduce the need for traditional RAG approaches in some scenarios.
5. Ethical Considerations and User Experience: The speakers touched on the importance of responsible AI implementation, particularly when dealing with sensitive information or deploying systems for program beneficiaries. Maria Dyshel mentioned her work on projects like Farm.Ink’s chatbot, highlighting the potential of RAG systems to make agricultural knowledge more accessible. Ensuring system safety, managing user expectations, and having fallback options for when the system fails to provide an answer were highlighted as crucial considerations.

This webinar provided a look at the current state of RAG technology, offering valuable insights for organizations considering implementing these systems. As AI continues to evolve rapidly, the balance between leveraging powerful tools like RAG and exploring emerging paradigms will be crucial for staying at the forefront of NLP applications in the development and humanitarian sectors.