What Is Retrieval-Augmented Generation (RAG)?

Retrieval-augmented generation (RAG) is an AI technique where an external data source is connected to a large language model (LLM) to generate domain-specific or the most up-to-date responses in real time.

LLMs are powerful, but their knowledge is limited to their pretraining data. This poses a challenge for businesses needing AI applications that rely on their own specific documents and data. 

RAG addresses this limitation by supplementing LLMs with external data. This technique retrieves relevant information from diverse structured and unstructured sources, including text, images, and video, to ground LLM responses in a company's proprietary data––improving accuracy and reducing hallucinations. This active retrieval—often facilitated by vector databases for efficient semantic search—enables LLMs to provide more informed, contextually relevant answers than if they relied solely on their pretraining.

In short, RAG works as follows:

• A user's query is encoded into a vector representation.
• The system searches for semantically similar data.
• The most relevant retrieved information is added to the model's context.
• A response is generated and grounded in the retrieved data.

RAG allows you to integrate specialized knowledge without retraining the LLM entirely, saving on compute resources.

Keyword search focuses on finding exact matches to the words or phrases a user enters, treating the query literally and with a limited understanding of synonyms or context. For example, a keyword search for "best running shoes for flat feet" may only return results containing that exact phrase. 

Conversely, semantic search aims to understand the meaning and intent behind the query by analyzing context and relationships between words and user history to deliver more relevant results. A semantic search for "best running shoes for flat feet" may return results for "stability running shoes," "arch support running shoes," or even reviews of specific shoe models suitable for flat feet, even if those exact keywords weren't used in the search query. 

Essentially, keyword search looks for the words, while semantic search looks for the meaning.

Information retrieval is the process of finding relevant documents or data based on a user query. It uses algorithms like BM25, TF-IDF, and vector search to return a list of sources, which users must then review manually for insights. Instead of just returning documents, RAG synthesizes a direct response using the retrieved data, reducing the need for manual interpretation. While information retrieval focuses on finding relevant content, RAG uses that content to generate context-aware, coherent answers in real time.

A canonical RAG pipeline has three phases: extraction (where data gets ingested and embedded), retrieval, and generation. Each phase is critical in ensuring a RAG pipeline retrieves precise, reliable, and relevant data.

During the extraction phase, enterprise data is collected, transformed, indexed, and stored in a vector database. In this phase, an embedding model converts textual, audio, or visual content into high-dimensional vector representations, enabling similarity-based searches. Embedding vectors are indexed (e.g.., as graphs) before storage for fast retrieval using approximate nearest neighbor (ANN) methods.

Retrieval identifies and retrieves relevant data using vector and keyword search techniques. Many RAG systems also use a reranking model to identify which of the retrieved data is most relevant. Appending a reranking model to the retrieval phase helps the RAG system improve the overall accuracy of the final response generation.

Finally, during the generation phase, LLMs combine the user’s prompt with retrieved data to craft answers that are simultaneously semantic and contextually precise. This combination gives RAG a distinct edge over LLM-only solutions.

A RAG architecture diagram showing three phases: data extraction, retrieval, and generation powered by NVIDIA NeMo™ Retriever microservices and accelerated with NVIDIA cuVS.

In a RAG pipeline, data extraction involves data collection, embedding generation, and indexing.

First, you must collect, parse, and clean your data—documents, PDFs, product catalogs, images, or even audio transcripts. Text is often chunked into paragraphs or sections to limit the context window and maximize retrieval accuracy. High-quality extraction involving accurate metadata and minimal duplication is crucial because even the most advanced LLMs will struggle if the underlying data is incomplete or disorganized.

Your data sources will indicate the ideal data collection method:

• Batch: Ideal for stable or static datasets. Process embeddings in bulk.
• Streaming: Suited for continually updated data (e.g., social media, news).

During the extraction phase, an embedding model transforms data into vector embeddings. Vector embeddings are numerical representations of data—such as text, images, or audio—mapped into a high-dimensional space. These embeddings capture the semantic meaning of content, enabling similarity-based retrieval in RAG. Items with related meanings are placed closer together in the vector space, allowing for fast and efficient searches. 

For example, in a search system, a query like "fast GPU for deep learning" retrieves documents with similar embeddings, ensuring contextually relevant results. High-quality embeddings are critical for accurate and meaningful retrieval in AI applications.

Finally, once embeddings are created and inserted, the system indexes the data in a vector database. Vector databases are at the core of RAG systems and are needed to efficiently store information as data chunks, each represented by a corresponding multidimensional vector produced by an embedding model. These databases can handle the complexities and specificities of vector space operations, like cosine similarity, offering several key advantages such as efficient similarity search, handling high-dimensional data, scalability, real-time processing, and enhanced search relevance.

Architecture showing data being extracted in a RAG pipeline and a GPU-accelerated vector database - powered by NVIDIA NeMo Retriever microservices and accelerated with NVIDIA cuVS.

Because RAG is not limited to text, it can also process images, audio, and video inputs by converting them into embeddings using computer vision and speech-processing models. This enables cross-modal retrieval, where users can query across data types. For instance, an ecommerce platform might embed both product descriptions and product images, so users can search visually (“Find images similar to this reference photo”) and textually. By supporting diverse data formats, enterprise and consumer applications are becoming smarter.

Multilingual models (embedding and LLM) used in RAG enable global accessibility for enterprise generative AI applications supporting queries and documents in different languages.

Retrieval identifies the most relevant data to enhance an LLM’s response. The process often begins with query rewriting, where the original search query is automatically refined. This can involve expanding it with synonyms, resolving ambiguities, or incorporating context from previous interactions to improve retrieval accuracy.

Next, the query is converted into an embedding—a numeric vector representation—using an embedding model. This transformation ensures compatibility with stored data embeddings, making it essential to maintain consistency between ingestion-time and query-time embeddings.

Finally, the system performs a similarity search, retrieving the top-k most relevant chunks by measuring vector distances using metrics such as cosine similarity, Euclidean distance, or dot product. ANN algorithms optimize this step by efficiently narrowing down potential matches. The retrieved content—whether text, image, or other data—then provides crucial context for the LLM’s final response.

Architecture diagram showing retrieval in a RAG pipeline with a GPU-accelerated vector database—powered by NVIDIA NeMo Retriever microservices and NVIDIA cuVS.

After retrieval, a reranking step follows to refine the results by prioritizing the most relevant data using a reranking model. This can be based on recency, domain relevance, or user preferences. Reranking models—whether heuristic-based or machine learning (ML)-powered—improve retrieval recall, ensuring that the LLM processes the highest-quality information first.

A reranking model refines retrieved results by prioritizing the most relevant chunks before passing them to the LLM. After initial retrieval, reranking models reorder content based on relevance signals, such as keyword frequency, semantic similarity, recency, or metadata alignment. They can be rule-based (heuristics like BM25), ML-driven (learned relevance models), or hybrid approaches that combine multiple factors. Effective reranking ensures that the LLM processes the most useful data first, improving response accuracy and efficiency in RAG systems.

To optimize LLM responses, there are advanced retrieval techniques that help enhance search precision by combining methods, managing large data, and adapting to query nuances.

Once relevant information is retrieved, the generation phase in RAG involves synthesizing a final response using an LLM. This process includes:

• LLM-Based Synthesis: The retrieved top-k chunks are inserted into the model’s prompt alongside the user query. The LLM then generates a response by grounding its output in the retrieved data, reducing hallucinations, and improving factual accuracy. Many enterprise RAG systems enhance transparency by citing or hyperlinking sources within the generated text.
• Summarization and Reasoning: In knowledge-heavy fields (e.g., medicine, law, research), RAG can summarize information from multiple sources, presenting it in structured formats like bullet points or concise paragraphs. This allows users to quickly grasp key insights without manually parsing multiple documents. Advanced RAG implementations also enable the model to reason over-retrieved content, synthesizing coherent and contextually rich responses.
• Output Validation: In high-stakes applications, an additional validation step ensures that the generated response remains faithful to the retrieved context. This can involve:
  • Comparing the output against the retrieved text to check for inconsistencies.
  • Using a secondary LLM to verify claims made by the first model.
  • Logging the generated response alongside its references for auditability and compliance.

By structuring responses around verified, retrieved information, RAG enhances the reliability and transparency of AI-generated content.

Advanced RAG pipelines benefit complex applications that require contextually rich responses, like customer support tools, legal services, and enterprise knowledge management. 

While simple retrieval can work for some applications, consider agentic AI workflows that include multiple AI agents working together to achieve a common goal. For example, software design, IT automation, and code generation are applications that warrant extensive data extraction and processing to ensure accuracy. An advanced RAG pipeline can include multiple passes of retrieval, reasoning, and tuning of response outputs based on criteria to ensure an optimal generated response that suits your business goals.

RAG enhances AI-generated responses by integrating external data retrieval, offering several key benefits:

• Improved Factual Accuracy: By retrieving information from a reliable, up-to-date data source, RAG reduces the model’s tendency to “hallucinate” and helps ensure that generated responses are grounded in real facts.
• Flexibility and Domain Adaptation: RAG can be easily tailored to different domains or knowledge bases without having to completely retrain or fine-tune the generative model. You simply change or update the data source(s) from which relevant information is retrieved.
• Up-to-Date Information: RAG enables the system to provide responses based on the latest data sources. Traditional LLMs are typically trained on static snapshots of data; RAG keeps the answers current by “pulling in” new data as it appears.
• Interpretability and Transparency: Users and developers can examine which documents or knowledge sources were retrieved and used in generating a response. This helps trace and verify the information provided, increasing trust and explainability.

These advantages make RAG a powerful approach for applications requiring up-to-date, reliable, and context-aware AI-generated responses.

The evolution of AI chatbots to AI assistants and then AI agents marks a significant milestone for enterprises and aiding the digital workforce. These generative AI-powered software applications use RAG to query databases in real time, delivering responses that are relevant to the context of the user’s query and enriched with the most current information available, without the need for retraining the underlying LLM. This advancement profoundly impacts user engagement, particularly in industries such as customer service, education, and entertainment, where the demand for immediate, accurate, and informed responses is paramount.

Industry applications that require AI-driven insights grounded in real-time, reliable data include:

There are multiple ways to improve the accuracy of a RAG pipeline, ranging from parametric methods (like fine-tuning) to non-parametric pipeline modifications.

Model Selection: Choosing the best model for your RAG pipeline has a heavy impact on the pipeline’s accuracy. Ensure you’re using the best model for each function (embedding model, reranking model, and generator) to increase the overall accuracy performance of many RAG pipelines.

Fine-tuning: A common way to improve RAG accuracy is to fine-tune your generator (the LLM responsible for producing final responses) and/or your embedding model (used to power retrieval and extraction). By capturing user feedback on the generated responses, you could set up a data flywheel that automatically fine-tunes your generator model.

Reranking: Using a reranking model helps you select the most relevant context for answering a user query. This method will add a small amount of additional latency but typically has an outsized impact on accuracy.

RAG “Hyperparameters”: By modifying and experimenting with different RAG hyperparameters, such as chunk size, chunk overlap, and embedding vector size, you can improve the RAG pipeline tremendously. These methods require robust evaluation (like through NeMo Evaluator Microservice) but can be extremely impactful on pipeline accuracy.

Query Augmentation: By adding methods like query transformation, rewriting, or otherwise, you can improve the effectiveness of your RAG pipeline by ensuring the query is suitable for the specific pipeline you have created for any domain.

RAG pipelines, which combine the strengths of LLMs with external knowledge sources, can experience performance gains by employing several techniques:

• Using smaller, fine-tuned models
  • Instead of relying exclusively on LLMs, employ specialized smaller models (fine-tuned for specific tasks) to generate final responses.
  • Reduces computational overhead and speeds up the generation process.
• Leveraging GPU-accelerated databases
  • Offload retrieval tasks to GPU-accelerated systems for rapid access to context.
  • Significantly cuts down latency during the data retrieval phase.
• Optimizing indexing structures
• Caching frequently accessed data