Webinar Recap: RAG: Built for Science, Scalable Across Industries
Artificial Intelligence is reshaping the way we process, analyze, and retrieve information. At Iris.ai, we are at the forefront of this transformation with our Retrieval-Augmented Generation (RAG) system, designed for scientific research and scalable across industries.
In our webinar, "RAG: Built for Science, Scalable Across Industries", we explored the transformative potential of Retrieval-Augmented Generation (RAG) systems in enhancing AI-driven research and decision-making. You can read the summarized information in this blog post, or watch a full webinar below.
What is a RAG System?
A RAG system improves AI-generated responses by incorporating three key stages: retrieval, augmentation, and generation. First, the system takes a user query and fetches relevant information from a database or storage. Then, the retrieved references are combined with the query, forming an enriched prompt. Finally, the augmented prompt is fed into the AI model, producing a more accurate and context-aware response.
Why Use RAG Instead of Direct LLM Queries?
LLMs need significant context to provide precise answers. Without external information, they often generate generic responses. RAG systems supply essential background data, improving accuracy and relevance. They also help mitigate hallucinations—instances where LLMs fabricate information with confidence. Because these models operate as black boxes, tracing the origin of an answer is challenging. By integrating a retrieval step, RAG ensures AI responses are based on verifiable data.
Another key limitation of LLMs is their static knowledge. These models rely on pre-trained data, which can become outdated over time. RAG allows models to incorporate new information dynamically without retraining. General-purpose LLMs also struggle with specialized terminology. A RAG system integrates proprietary data, improving domain relevance. Furthermore, given the constant evolution of knowledge, retraining an LLM is costly and slow. A RAG system allows real-time updates by fetching the latest information from external sources.
Core Retrieval Methods in RAG Systems
A RAG system relies on effective retrieval methods to find relevant information. The most common approach is vector databases, which store text as numerical representations (embeddings). Similar concepts are mathematically close, allowing the system to retrieve semantically relevant documents. This method is excellent for abstract comprehension and contextual search but can be computationally expensive and may struggle with proper nouns and industry-specific terminology.
Another method is knowledge graphs, which store structured relationships between entities, such as "Mona Lisa was painted by Leonardo da Vinci." This approach excels at connecting related facts across documents and is particularly useful for entity-based queries. The process of entity extraction and linking can be automated using LLMs. However, while this method offers flexibility, it requires a certain degree of repeatability in entity-relationship structures for meaningful patterns to emerge. As a result, knowledge graphs work best with collections of documents that share similar structural characteristics.
Traditional keyword-based search is another common retrieval method, matching user queries to exact words in stored text. While excellent for filtering data by specific terms and retrieving proper nouns, it lacks deep understanding of query intent and struggles with abstract or paraphrased queries.
Metadata retrieval focuses on extracting and utilizing structured data embedded within documents, such as authorship, publication dates, categories, tags, and other predefined attributes. For example, when searching for documents published after a certain date or authored by a specific individual, metadata retrieval provides fast and precise results.
Semantic retrieval goes beyond keyword matching to understand the meaning and context behind queries and documents. By leveraging natural language understanding (NLU) techniques, semantic retrieval interprets user intent and retrieves documents that align with the conceptual meaning, even if exact keywords don’t match.
Iris.ai Multi-RAG Approach
Given the limitations of single retrieval methods, at Iris.ai we developed Multi-RAG, a system that dynamically selects the best retrieval approach based on the user query. The architecture is modular and agent-driven, ensuring that each stage of the process is optimized for accuracy and relevance. This approach combines multiple retrieval methods, including vector search, knowledge graphs, semantic search, metadata retrieval and keyword search, to improve accuracy.
A key innovation in the solution is its use of multiple specialized agents that coordinate the RAG process. The Strategy Selection Agent examines the incoming query and decides which retrieval strategy or combination of strategies is most appropriate. After the initial retrieval, another agent evaluates the relevance and completeness of the retrieved document set. Its role is to decide whether additional retrieval passes are necessary or if the information set is sufficient for generating an answer. Once relevant documents have been gathered and evaluated, Prompt Optimization Agent optimizes the query prompt before it is passed to the LM.
Although the system performs several steps (retrieval, evaluation, prompt optimization, LM inference), the design prioritizes accuracy over minimal latency. For use cases where high-quality, context-rich answers are required, the slight increase in latency is acceptable. However, the optimized retrieval strategies and indexing methods ensure that even with these extra processing layers, performance remains competitive when compared to simpler RAG systems that can only handle much smaller document sets.
Why We Built Multi-RAG
Many AI systems perform well in proof-of-concept settings but struggle in real-world applications. Single retrieval systems often hit accuracy limits, making them unreliable for business-critical use cases. Multi-RAG dynamically adapts to different queries, increasing accuracy by leveraging multiple retrieval strategies.
Use Cases for Multi-RAG
1. Handling Unpredictable Data & Queries
Multi-RAG is particularly useful in handling unpredictable data and queries. It is ideal for platforms where users upload diverse datasets, requiring flexible retrieval approaches. For instance, a sales platform where each client has unique data structures can benefit significantly from this system.
2. Domain-Specific Information Retrieval
Domain-specific information retrieval is another critical application, especially in industries with specialized terminology such as engineering, patents, life sciences, and corporate knowledge management. General-purpose LLMs struggle with these fields, but a RAG system tailored to domain-specific data can improve precision.
3. High-Accuracy & Compliance-Critical Applications
Multi-RAG is valuable for high-accuracy and compliance-critical applications. Legal and regulatory research, pharmacovigilance, and financial risk analysis require precise data extraction.
- Legal & Regulatory Research (ensuring compliance with legal frameworks)
- Pharmacovigilance (tracking adverse drug reactions)
- Financial & Risk Analysis (accurate entity recognition for decision-making)
RAG as a Service (API Offering)
We provide our Multi-RAG system as an API for businesses needing scalable and adaptable AI-powered retrieval. This service supports data ingestion and indexing, converting documents into structured formats for retrieval. It also allows for fine-tuning and optimization, adjusting retrieval methods based on use case needs. Query processing is another key feature, as the system dynamically selects the best retrieval method for each question. Additionally, users can choose between retrieving raw documents or receiving AI-generated responses. The solution is designed to be cloud-agnostic. It can be deployed on public clouds, within virtual private clouds, or even on-premise, depending on customer needs. The system allows for custom scripts or code integrations so that internal behavior can be monitored, logged, and even modified for further optimization.
Conclusion
Multi-RAG addresses the inherent weaknesses of single retrieval methods, making it a powerful tool for industries requiring high-precision AI-generated insights. Whether you’re developing a chatbot, an internal research tool, or a compliance system, Multi-RAG adapts dynamically to ensure the best possible answers.
Watch the full webinar below 👇