Navigating RAG Optimization: Evaluation-Driven Approach for Generative AI Implementation

Navigating RAG Optimization: Evaluation-Driven Approach for Generative AI Implementation

Join Atita and Diana Emory as they navigate the complexities of Rag Optimization, an evaluation-driven approach to building and productionizing generated AI that combines searching and retrieving vast amounts of information.

• 1. Atita and Diana from Quadrant and Quo AI will discuss Navigating Rank Optimization with an Evaluation-Driven Compass, focusing on RAG (Retrieval-Augmented Generation).
• 2. RAG combines information searching and retrieving capabilities from a knowledge source, typically a vector database, to generate relevant and coherent responses using a large language model.
• 3. There are various ways to implement RAG, including:
• * Naive RAG: Splitting documents using a specific chunking strategy, processing document embedding, storing them in a vector database, retrieving the most relevant document chunks for user queries, an
• * Advanced versions involve query enhancements like expansion or rewriting and post-retrieval treatments like result reranking or Fusion.
• 4. Vector databases are essential for all RAG implementations. Quadrant is an open-source vector search database built on Rust, designed for large-scale data and AI applications.
• 5. Challenges in RAG include:
• * Data processing issues like missing information or failed extraction from the source.
• * Chunking strategy determination and embedding model selection.
• * Relevancy is an unsolved problem in information retrieval, making determining relevant documents, retrieval size, and order unskippable.
• * Response generation can face challenges like incorrect, incomplete answers, straying from the context, or ambiguous/vague queries.
• 6. Improvement techniques for RAG include:
• * Ensuring data quality by adopting data cleaning and advanced extraction methodologies.
• * Using domain-specific embedding models to better understand terminologies.
• * Leveraging metadata filtering during retrieval to filter out irrelevant documents.
• * Determining an optimum context size for generating helpful responses.
• * Utilizing suitable indexing algorithms like HNSW, BM25, or graphs for optimal performance.
• 7. Evaluation is crucial in the RAG pipeline to accurately measure progress and ensure optimal performance. It helps iteratively refine applications and make informed decisions for better handling of
• 8. Quadrant's evaluation solution enables developers to measure the effectiveness of their LLM products accurately by customizing evaluations for specific domains and data sets.
• 9. The platform accelerates the experimentation process with an evaluation data set containing realistic examples of inputs and expected outputs for AI solutions.
• 10. Users can quickly experiment, iterate, and optimize RAG solutions using Quadrant's evaluation data set, which handles full orchestration, prompt formatting, LM execution, and metric computations.
• 11. Evaluation-informed changes to optimize RAG systems can be made using a demo walkthrough provided by Quadrant, which shows a workflow for building a RAG solution for question answering on Quadrant
• 12. When optimizing a RAG system, it is essential to consider what you are optimizing for, such as helpful answers with minimal inaccurate information (hallucinations) to minimize misguiding users.
• 13. Focus metrics should include context relevance (whether necessary information to answer questions is in the retrieved documents) and chunk relevance (usefulness of retrieved information vs. noise)
• 14. Faithfulness, a hallucination metric, is crucial for the focus of this talk on optimizing the retrieval side of RAG.
• 15. Starting with a simple naive RAG implementation can help better optimize data processing and vector database setup by choosing reasonable embedding models and chunking parameters.
• 16. Testing additional context to answer questions may require increasing chunk per parameters and observing improvements or drops in text quality and faithfulness metrics.
• 17. Switching to different embedding models or LMs can improve performance across all metrics, but it is crucial to evaluate the results of such changes.
• 18. Hybrid search, which combines sparse and dense vectors, can help capture documents that share similar terminology, particularly for domain-specific jargon, acronyms, and special terminology.
• 19. Continuously evaluating RAG systems and making incremental improvements based on data-driven insights is essential to optimize performance and minimize hallucinations.
• 20. Quadrant provides resources and references for further learning and invites the audience to get in touch with them for questions and discussions related to RAG optimization and evaluation.

Source: AI Engineer via YouTube

❓ What do you think? What is one critical factor that can greatly impact the performance of a Rag system, and how can it be optimized for better results? Feel free to share your thoughts in the comments!