Why Retrieval-Augmented Generation (RAG) matters
Large language models (LLMs) have transformed AI with their ability to generate human-like text, but they have notable limitations. Because an LLM’s knowledge comes from its training data (often months or years old), it can struggle with answering questions about recent events or company-specific information. Additionally, an unchecked LLM may give hallucinated answers and/or sources.
Retrieval-Augmented Generation (RAG) addresses this gap by grounding the model’s responses on an external knowledge source. RAG is an AI framework that retrieves relevant context from a knowledge base and feeds it into the LLM to improve the quality and accuracy of the generated response. By incorporating domain-specific, up-to-date data at query time, a RAG architecture can dramatically increase the relevance and factual accuracy of AI outputs. This means the model’s answers are backed by real documents or database records, reducing the chance of misinformation and increasing trust.
RAG expands an LLM’s abilities without requiring expensive retraining, allowing the system to remain current and precise by pulling in fresh data on demand. And importantly, it lowers the risk of AI hallucinations by verifying them against a trusted database.
In our work at Dattell, we’ve used OpenSearch to index structured and unstructured data, enabling LLMs to ground their responses in trusted sources. Its support for vector search, filtering, and its inclusion of RAG processing tools makes OpenSearch a great fit for production-grade RAG pipelines.
How companies use RAG to enhance AI
RAG is quickly becoming a cornerstone of real-world AI solutions. Many companies, like ours, are adopting RAG to make their AI systems more factual, context-aware, and useful.
Across industries, organizations are leveraging RAG to boost AI performance in various use cases:
- Customer support and chatbots: Businesses are augmenting support chatbots with RAG so they can pull answers from internal knowledge bases, policy documents, or FAQs. Instead of replying with generic text, a RAG-powered bot can retrieve the exact solution or information the user needs from the company’s documentation in real time. This results in faster, more accurate answers, improving customer satisfaction. Many enterprises, from e-commerce retailers to banks, have deployed RAG-based chatbots to handle complex customer queries that previously required a human agent, reducing support load and response times.
- Knowledge retrieval for employees: RAG is used to build intelligent internal assistants. For example, a consulting firm might enable consultants to query an AI assistant that leverages RAG to search corporate wikis, technical manuals, or past project archives. This internal RAG tool can instantly retrieve relevant snippets (e.g., a specific policy or design document) and have the LLM summarize or explain them.Organizations often face information silos. RAG-based search assistants help surface the right info quickly, saving employees time and boosting productivity.
- Domain-specific Q&A: In specialized fields like finance, healthcare, and law, companies use RAG to inject domain knowledge into AI.Financial services firms, for instance, use RAG to provide advisors or customers with up-to-date financial data and personalized reports. The LLM generates a narrative but all figures and facts are fetched from databases or market data feeds in real time.Healthcare providers employ RAG-based systems to answer patient questions or assist clinicians, pulling in the latest medical research or patient records securely to ensure accuracy.Legal departments use RAG to retrieve relevant case law or regulations, which the LLM can then summarize or analyze. Technology companies even use RAG to help developers by retrieving technical documentation and code examples when asked. And other AI tools for lawyers, like Ask Jura, are employing RAG to reduce hallucinations and increase the accuracy of answers for lawyers working at firms.
- Personalization and recommendations: E-commerce and content platforms have begun using RAG to augment recommendation engines. An AI that generates a product recommendation or marketing email can use RAG to pull in contextual data. For example, the latest user activity, inventory status, or trending products can be used to tailor suggestions.
By enhancing accuracy and contextual relevance, RAG is enabling businesses to deploy AI that users can trust – whether it’s a customer-facing chatbot that always provides correct answers or an internal tool that accelerates research with up-to-date data.
OpenSearch features for RAG
To implement Retrieval-Augmented Generation effectively, you need a robust retrieval system at the core, and that’s where OpenSearch excels. OpenSearch is a widely adopted open source search and analytics engine, and it can double as a high-performance vector database for RAG use cases.
Below we outline key OpenSearch features and plugins that make it ideal for powering RAG workflows:
- k-NN vector search engine: OpenSearch supports k-Nearest Neighbors (k-NN) search that enables fast vector similarity search across your documents. This is critical for RAG because it allows semantic retrieval.
- Neural search and embedding integration: Beyond basic vector search, OpenSearch has neural search capability that integrates machine learning models (often deep neural networks) into the indexing and querying process. With the neural search plugin, you can automatically generate vector embeddings for your text data at ingest time using an encoding model.
- RAG search pipeline processor: Starting with OpenSearch 2.12, the engine introduced a dedicated Retrieval-Augmented Generation processor that can be plugged into search pipelines. The processor intercepts queries, retrieves from a history of previous messages, and sends the prompt to the LLM. That LLM response then gets added to the conversational memory.
This RAG processor supports connecting to OpenAI and Amazon Bedrock models.
- RAG tool: OpenSearch’s RAG tool supplements user questions with its own relevant knowledge stored within OpenSearch. The RAG tool supports both neural search and neural sparse search.
- OpenSearch Assistant Toolkit: The OpenSearch Assistant Toolkit allows users to build AI-powered assistants to use within their OpenSearch Dashboards. The toolkit includes agents to interface with an LLM and execute tasks (e.g., summarization).
OpenSearch provides the key building blocks for RAG: a fast vector search engine (kNN), built-in neural embedding pipelines, and specialized RAG plugins to interface with LLMs. OpenSearch can store your knowledge base, index vectors for semantic search, retrieve relevant context, and seamlessly integrate with generation models.
Navigating RAG complexity
Building a RAG solution with OpenSearch brings immense benefits, but it also introduces new complexities that go beyond a standard search deployment. Organizations must manage not only the search index but also the vector mappings, model integrations, and overall system performance to ensure the LLM responses remain fast and accurate. Some challenges include:
Infrastructure scaling and performance. RAG workloads can be demanding. Vector searches are computationally intensive, and calling LLM APIs for each query adds latency. OpenSearch clusters must be tuned for low-latency k-NN queries (e.g., choosing the right ANN algorithm, setting correct index.knn parameters, memory management for vectors).
You also need to ensure the search cluster can handle spikes in query volume without slowdowns. Designing a highly available architecture with proper indexing strategy, sharding, and failover is critical.
Relevance tuning and data prep. Simply indexing documents and relying on the default vector model might not yield the best results. The embedding models need to be fine-tuned so that the retrieved context is truly relevant to the questions.
There’s also the task of preparing and updating the knowledge base. For example, data preparation could include filtering out irrelevant content, segmenting documents into passages, and keeping embeddings up-to-date as data changes. Balancing dense vs. sparse retrieval (hybrid search) may be needed for optimal accuracy. All these require hands-on experience with OpenSearch’s mapping, analyzers, and ML integration to get right.
Integration and pipeline orchestration. Setting up the end-to-end pipeline involves multiple components (OpenSearch plugins, external model APIs or on-prem models, application logic). Search pipelines or agents (like the RAG processor or RAG tool) need to be configured correctly and efficiently. Things like conversation memory indexing, or how to securely connect OpenSearch with external LLM services (API keys, network considerations) must be handled with care.
Monitoring and troubleshooting. When you combine generative AI with search, you inherit all the failure modes of both. Diagnosing issues requires robust monitoring. OpenSearch cluster monitoring needs to include tracking query latency, memory usage, and recall accuracy of vector searches, among other metrics. And you’ll need monitoring for the LLM service, including rate limits and prompt failures.
Considering these complexities, many businesses choose to engage a specialized partner for OpenSearch and RAG implementation. For example, my company, Dattell, assists teams with setting up OpenSearch indices for vector search, integrating the appropriate ML models, and configuring RAG pipelines.
In conclusion, Retrieval-Augmented Generation is a powerful technique to elevate your AI applications with real-time knowledge. OpenSearch, with its open-source flexibility and rich feature set, is an ideal platform to implement RAG – providing the necessary retrieval tools (vector search, neural plugins, RAG pipelines) within a proven search engine. With OpenSearch, you get an open source RAG toolset that can be tailored to your data and deployed anywhere.
