Improving RAG to answer complex questions

Simple Retrieval-Augmented Generation (RAG) systems combine the power of language generation with targeted information retrieval to provide effective answers to direct questions. However, when a query is complex—requiring a deeper understanding across multiple sources or a broader connection between pieces of information—basic RAG systems often fall short. To elevate RAG, we can integrate advanced techniques like semantic chunking, knowledge graphs, and agentic workflows, enabling RAG systems to deliver richer, more complete answers to complex questions.

The Challenge of Complexity in RAG

Consider a healthcare chatbot that’s tasked with answering patient questions using multiple data sources, such as electronic health records, previous diagnoses, and physician notes. A simple RAG system may retrieve chunks of information about specific symptoms but miss critical, related details, such as past treatment plans or underlying health conditions. The result is an incomplete response that, while factual, lacks the comprehensive context needed for a healthcare setting.

This example highlights a key limitation of conventional RAG setups: while they perform well for straightforward questions, they struggle with layered queries that draw on multiple sources. By employing more advanced techniques, we can equip RAG systems to provide deeper, more connected responses.

Key Techniques to Elevate RAG

Enhancing both the retrieval and response processes with specific techniques enables RAG to handle complex questions more effectively. Here’s how:

1. Improved Chunking Techniques

A key limitation in simple RAG systems is how content is divided into chunks for retrieval. Basic chunking methods often divide content at fixed intervals, losing important context along the way. Advanced chunking techniques create a more meaningful structure:

• Semantic Chunking: This method divides content by meaningful sections, such as paragraphs or topical boundaries, instead of arbitrary word counts. Semantic chunking preserves the flow and context within each chunk, making retrieved information more coherent.

• Overlapping Chunking: With overlapping chunking, the end of one chunk overlaps with the beginning of the next, capturing details at chunk boundaries twice. This ensures that essential context isn’t lost, allowing the system to piece together more complete answers.

2. Knowledge Graphs

Knowledge graphs provide a structured way to represent information by mapping entities (e.g., “symptom,” “diagnosis,” “treatment”) and their relationships. For our healthcare chatbot, a knowledge graph would map connections between symptoms, diagnoses, and treatment protocols, helping the system identify links across data sources. When a user asks a question about a recurring symptom, the RAG system can retrieve related notes on previous treatments or underlying conditions, providing a more holistic response.

3. Enhanced Retrieval with Contextual Awareness

Embedding contextual awareness into the retrieval process allows RAG systems to focus on the specific theme of the question. For example, if a patient asks about treatment options for their current symptoms, adding "treatment history" as a contextual term refines the retrieval process, bringing in relevant treatment information while filtering out unrelated data. This technique makes responses more targeted and relevant for complex healthcare questions.

4. Breaking Down Complex Questions

Complex questions often cover multiple facets, and addressing each part individually can yield more accurate results. If a patient asks about both symptoms and long-term treatment outcomes, each element can be addressed independently. By breaking down the query, the RAG system can provide a comprehensive answer that covers each aspect in depth, ideal for detailed healthcare inquiries.

5. Agentic Workflows

Agentic workflows allow RAG systems to adapt to the question’s complexity dynamically. For some queries, the agent might use a knowledge graph to retrieve contextually relevant data, while for simpler questions, a straightforward RAG chain could suffice. This flexibility ensures that each query is processed with the most effective tools, making advanced RAG systems adaptable to a range of question complexities in healthcare and beyond.

XpoRAG: Advanced architecture designed to handle complex scenarios across diverse data sources

Challenges and Practical Tips for Advanced RAG

While these advanced techniques enhance RAG’s retrieval capabilities, they also bring challenges that require careful consideration:

• Computational Costs: Techniques like semantic chunking and knowledge graphs increase processing demands, so it’s essential to optimize computational resources and balance accuracy with efficiency.

• Data Management: Knowledge graphs and overlapping chunks need regular updates and monitoring to ensure accuracy. Incorporating well-structured metadata, like timestamps and relationships, enhances organization and ensures efficient retrieval. Building streamlined data pipelines and conducting routine audits help maintain a reliable and responsive RAG system.

Best Practices for Optimizing Advanced RAG

• Dynamic Chunk Sizes and Overlaps: Adjust chunking parameters to capture complete context without creating unnecessary redundancies.

• Monitor Retrieval Accuracy: Regularly test retrieval quality across varied question types to ensure consistent performance and make adjustments as needed.

• Streamline Agentic Workflow Configurations: Configure agents to adapt dynamically based on query complexity, optimizing workflows for speed and relevance.

We’re solving the challenge of answering complex questions, but the journey doesn’t stop there. Creating and managing these RAG pipelines, especially with vast amounts of unstructured data, brings new layers of complexity. At XponentL, we tackle this by using knowledge graphs to structure and connect information, allowing the system to efficiently retrieve the most relevant details.

As questions grow more intricate, RAG systems must evolve accordingly. While advanced setups can yield ideal results, they also increase computational and maintenance demands. This is where Xpo comes in: we focus on tailoring RAG solutions to each client’s needs, striking the right balance between sophistication and efficiency. By delivering adaptable, high-quality RAG systems, we enable businesses to answer complex queries with precision and impact—without unnecessary overhead.