What is Retrieval-Augmented Generation?

Retrieval-augmented generation enhances large language model prompts with relevant data for more practical, accurate responses.

Retrieval-augmented generation (RAG) is a software architecture that combines the capabilities of large language models (LLMs), renowned for their general knowledge of the world, with information sources specific to a business, such as documents, SQL databases, and internal business applications. RAG enhances the accuracy and relevance of the LLM’s responses. RAG uses vector database technology to store up-to-date information that’s retrieved using semantic searches and added to the context window of the prompt, along with other helpful information, to enable the LLM to formulate the best possible, up-to-date response. This architecture has become famous for many use cases, including its ability to offer detailed, relevant answers by integrating the best of both worlds—knowledge from LLMs and proprietary business data.

AI chatbots and web apps use LLMs like Llama2 and GPT, which have been meticulously trained on extensive collections of information to generate responses that satisfy user prompts. With their deep understanding of language nuances and generative capabilities, these LLMs serve as the foundation for the RAG architecture.

Step 1: The first step in implementing a RAG system is to locate the knowledge sources inside the business that the project will use. This data may include metadata, text, images, videos, tables, graphs, and so on. The enterprise-specific data is preprocessed and split into data chunks by a data-processing service. Next, the chunks are fed into the embedding model, creating a vector, a numerical representation, of each data chunk’s meaning and nuance. These vectors and their corresponding data chunks get stored in a vector database for later retrieval.

Step 2: Upon receiving a chatbot or AI application query, the system parses the prompt. It uses the same embedding model used for data ingestion to create vectors representing parts of the user's prompt. A semantic search in a vector database returns the most relevant enterprise-specific data chunks, which are placed into the context of the prompt. Other data chunks—from information retrieval systems such as SQL databases, other business-critical applications, and AI models—and additional LLM instructions are also retrieved before the augmented prompt is sent to the LLM. LangChain and LlamaIndex are popular open-source programming frameworks that provide automation for creating AI chatbots and RAG solutions.

The arrival of AI chatbots marks a significant milestone in providing users with access to business-specific data in a question-and-answer, conversational style using natural language. When ChatGPT burst upon the scene with its enormous GPT-3 LLM, the excitement around AI chatbots became widespread. At the time, GPT-3 was confined to using data that it was trained with. Modern AI chatbots use either proprietary or open-source LLMs, such as GPT-3, Llama, or Mistral with RAG, so current, business-specific data sources can be used to enhance the prompt fed to the LLM, increasing its relevance and usefulness.

AI chatbots 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.

When deploying LLMs within the fabric of enterprise AI solutions, a significant challenge is the phenomenon of hallucinations. These are instances where responses generated by the LLMs, while appearing logically coherent and plausible, diverge from factual accuracy. This issue risks the integrity of enterprise decision-making processes and undermines the reliability of AI-driven insights.

With RAG, the LLM is provided with additional instructions and relevant data chunks in the context window of the prompt to better inform it, which can reduce hallucinations but doesn’t eliminate them. Traditional hallucination mitigation techniques are still required. Most enterprise deployments also use guardrails to mitigate harmful user interactions.

A combination of techniques leads to more efficient and targeted responses for specific enterprise needs, equipping LLMs for real-world business scenarios:

• Foundation models are LLMs developed through a labor-intensive and costly process, using machine learning and extensive collections of widely accessible data. This process demands significant hardware and data resources.
• Prompt engineering is a technique used to enhance a user's original prompt by adding context before sending it to the LLM.
• Alignment techniques:
  • Fine-tuning the LLM improves its fitness for a particular task by training it with business-specific information, vocabulary and skills.
  • Reinforcement learning from human feedback (RLHF) improves models with direct human feedback on alternative responses to a prompt, tuning the LLM’s response in matters of taste or style where there’s no definitive measure of accuracy.
  • RLAIF improves the instruction-following abilities of LLMs. It leverages reinforcement learning techniques and AI-generated feedback to fine-tune language models.
  • DPO is a method for fine-tuning LLMs to align them with human preferences without relying on sampling from the language model during training.
  • SteerLM is an approach for dynamically guiding—through real-time adjustments and feedback mechanisms—large language models to generate responses more aligned with user preferences and intentions.
• RAG adds relevant data chunks to the user's prompt, typically retrieved from a vector database or other information sources within the business, before sending it to the LLM.

These techniques improve the accuracy and relevance of the prompt sent to the LLM, enabling it to deliver the best possible responses to user queries.

Embedding models, such as Word2Vec for words or BERT for sentences, convert data chunks like words, sentences, graphics, and tables into multidimensional numerical vectors, capturing their meaning and nuance in a vector space. In a well-trained embedding model, items with similar meanings are positioned closely in the vector space, indicating their interchangeability or relationship.

Embedding vectors are used in various natural language processing (NLP) tasks like text classification, sentiment analysis, and machine translation, enabling machines to process and understand language more effectively. Embedding models serve as a bridge, allowing for a more nuanced interaction between technology and human language by encoding semantic and contextual relationships into a manageable, numerical format. The efficiency and accuracy of information retrieval hinges on the quality and sophistication of the embedding model, making it a critical element in the RAG ecosystem.

Vector databases are at the core of RAG systems. They’re needed to efficiently store business-specific information as data chunks, each represented by a corresponding multidimensional vector produced by an embedding model. The data chunks stored in the vector database may be text, graphics, charts, tables, video, or other data modalities. These databases can handle the complexities and specificities of vector space operations, like cosine similarity, offering several key advantages:

• Efficient similarity search: They enable quick searches for the top-K vectors closest to a query vector, crucial for semantic searches and recommendation systems.
• Handling high-dimensional data: As the number of features of interest in the data increases, it becomes difficult to deliver the fastest performance using traditional SQL databases.
• Scalability: Vector databases can be run across many GPU-accelerated servers to deliver the desired performance for data ingestion or similarity searches.
• Real-time processing: RAG applications, such as AI chatbots, rely on vector databases to provide up-to-date business information to send to the LLM, so it can better satisfy user queries.
• Enhanced search relevance: They provide more relevant results by understanding semantic relationships, improving content discovery and user experiences.

These features make vector databases integral to RAG, supporting efficient operations involving complex data.

The vector search mechanism is foundational to the operation of RAG systems, underpinning the swift and efficient retrieval of enterprise-specific information. This process encompasses a series of intricate steps, from the initial chunking and conversion of data into vectors using embedding models to algorithms such as approximate nearest neighbor (ANN) search to retrieve the top-K matching vectors from a vector database. These algorithms, often requiring GPU acceleration, are vital to navigating the extensive datasets typical of enterprise environments, ensuring that the most relevant information is retrieved quickly and accurately.

Continuous refinement, feedback, and updating are essential features of RAG systems, enabling them to improve and evolve. User feedback can be leveraged in the system to create more accurate and relevant responses in the RAG system. New data is perpetually incorporated into the vector database, providing the system and the user with the most pertinent material. This dynamic process of continuously updating underlying business information ensures RAG systems can deliver high-quality, contextually appropriate responses, without the need for costly re-training of LLMs.

Many elements of the RAG pipeline are GPU-accelerated, including scrubbing source data, creating embeddings and indexes, doing similarity searches in the vector database, and the operations performed by the LLM to respond to a prompt. Through the use of the NVIDIA RAPIDS™ RAFT library, TensorRT™, TensorRT-LLM, and Triton™ Inference Server, Transformer Engine, Tensor Cores, and other GPU-acceleration technologies, RAG applications benefit from efficient use of the underlying hardware. This acceleration is crucial for maintaining the high-performance standards required by RAG systems, facilitating swift adaptation to emerging data trends, and ensuring that responses generated by the system remain accurate, relevant, and grounded in the most current information available.

In data governance, RAG systems prioritize managing domain-specific LLM training data and knowledge base data, particularly when handling proprietary or sensitive data sources. Adherence to privacy regulations and ethical standards and implementing robust security measures are fundamental to maintaining the integrity and trustworthiness of RAG applications. Through a comprehensive framework that includes privacy, security, data quality, ethical data usage, and lifecycle management, RAG systems are committed to ethical AI development and deployment, ensuring that data is used effectively, responsibly, and transparently. NVIDIA NeMo™ Guardrails are a highly recommended capability that ensures LLMs don’t generate the wrong content.

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