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One of the latest advancements in natural language processing (NLP) is retrieval-augmented generation (RAG), a technique that combines the strengths of information retrieval and natural language generation (NLG). RAG can reshape how software is conceptualized, designed and implemented, ushering in a new era of efficiency and creativity powered by generative models.

What is retrieval-augmented generation (RAG)?

Retrieval-augmented generation (RAG) is a natural language processing (NLP) model that combines two key components: a generator and a retriever.

Generator: This part creates new content, such as sentences or paragraphs, usually based on large language models (LLMs).

Retriever: This part retrieves relevant information from a predetermined set of documents or data.

In simple terms, RAG uses the retriever to find useful information from a vast collection of texts, and then the generator uses that information to augment its LLM-based training to create new, coherent text. This approach helps improve the quality and relevance of AI-generated content by leveraging new and often more domain-specific knowledge outside of the vast dataset used to train the original LLM. It's commonly used in tasks like answering questions or summarizing text.

RAG integrates these two processes, allowing developers to use a wealth of existing knowledge to augment LLMs to enhance the generation of new, contextually relevant content.

What does the data look like?

Data is the lifeblood of large language models, generative AI (gen AI) models and AI applications and data is used in various ways to train, validate and improve the performance of these models across different domains. NLP models and RAG use a type of training data called “vector data” to determine relationships between datasets.

What is vector data?

You may have heard of vector data in geographic information systems (GIS) and mapping. A number of fields use vector data today, including geography, urban planning, environmental science and transportation. It allows for the accurate representation, analysis and visualization of spatial information, helping users understand and make decisions based on geographic data. Vector data illustrates the relationship or space between things, such as how far apart one city is from another.

How do NLP and RAG use vector data?

NLP and RAG do not use vector data in the traditional GIS or spatial analysis sense, but vector representations are crucial for various tasks within these systems. In this framework, vector data typically refers to numerical representations of words, sentences or documents in a high-dimensional vector space.

These numerical representations are used in models, commonly called “embeddings.” These embeddings capture semantic and syntactic relationships between words or text segments. For example, high-dimensional vector data can be fed into models such as IBM’s or Hugging Face, which specialize in converting data into embeddings by transforming complex data into numerical forms that computers can understand.

While the term "vector data" in RAG might not refer to geographic vectors, representing text as vectors is central to many aspects of NLP and RAG, including representation learning, retrieval and generation. This training data enables models to process and manipulate text meaningfully, facilitating tasks like question answering, summarization and dialogue generation.

How RAG can be used in software development

1. Information retrieval

Information retrieval plays a crucial role in software development. Developers often need to access many resources, including documentation, code repositories, forums and research papers. RAG streamlines this process by automating the retrieval of relevant information, saving time and providing developers with access to the most up-to-date, accurate and contextually-relevant information.

2. Natural Language Generation

Once the relevant information is retrieved, RAG's natural language generation component takes center stage. This involves creating human-readable text based on the retrieved data. In software development, this could manifest as code snippets, documentation or even interactive guides. The generated content is not merely a copy-paste of existing information, but is tailored to the developer's specific needs.

3. Iterative refinement

What sets RAG apart is its iterative refinement process. Developers can interact with the generated content, providing feedback and refining the output. This two-way interaction hones the final result so it is more accurate and better aligns with the developer's intent and coding style. It's an iterative approach that bridges the gap between the vast sea of information and the unique requirements of a given project.

Software development use cases for retrieval-augmented generation

Use case 1: Code generation

RAG can be a game-changer in code generation. Developers can describe high-level requirements or logic, and the system can retrieve relevant code snippets, adapting them to fit the specific context. This accelerates the coding process and encourages best practices.

Use case 2: Documentation

Documentation is a vital aspect of software development, often neglected due to time constraints. RAG simplifies the creation of documentation by pulling information from relevant sources and automatically generating coherent, developer-friendly documentation.

Use case 3: Troubleshooting and debugging

When faced with a coding challenge, developers can use RAG to search for solutions and receive context-aware suggestions. This can significantly speed up the debugging process and reduce downtime.

Leveraging RAG for hybrid cloud computing

Developer operations (DevOps) and machine learning operations (MLOps) teams can leverage RAG in a hybrid cloud environment—for example, to improve data management, model training, documentation, monitoring and resource allocation processes—to increase the efficiency and effectiveness of machine learning operations.

Data and documentation

RAG can be used to retrieve relevant data from both on-prem and cloud-based data sources. This is particularly useful in a hybrid cloud environment where data may be distributed across multiple locations. By more effectively retrieving and augmenting data, MLOps helps machine learning models access diverse and comprehensive datasets for training and validation.

RAG can also aid in automating documentation and knowledge-sharing processes within MLOps workflows. RAG systems can automatically generate documentation, reports and summaries of machine learning experiments, model evaluations and deployment procedures using NLG capabilities. This helps maintain comprehensive activity records and simplifies knowledge transfer between team members.

Resource allocation and optimization

RAG techniques can also be integrated into workflows to enable adaptive resource allocation and scaling in a hybrid cloud environment. For example, MLOps teams can dynamically allocate computational resources across on-premises infrastructure and cloud-based platforms to optimize model training, inference and deployment processes by generating insights into model performance and resource utilization.

The growing AI ecosystem

There is a growing ecosystem of data products and generative models for developers looking to harness RAG. One notable example you may have heard about is from OpenAI, the company behind ChatGPT. OpenAI's RAG assistant is currently in beta release and is part of the broader family of models developed by OpenAI.

Organizations and developers can also implement their versions of RAG using an ecosystem of data tools and models to create an environment with an enhanced security posture for specific use cases. In addition, the growing partnerships in this ecosystem are helping MLOps teams get started quickly and focus on delivering business outcomes rather than spending their time troubleshooting and maintaining a complex array of standalone technologies.

Learn more

Dell Technologies and Red Hat have partnered to deliver a full-stack AI/ML solution built on Dell APEX Cloud Platform for Red Hat OpenShift with Red Hat OpenShift AI. Using a set of vectorized documents, OpenShift AI on the DELL APEX Cloud Platform uses an LLM with RAG to create a digital assistant that not only contains subject information unique to an organization but also provides up to date answers to its users.

Red Hat continues to build its software and hardware partner ecosystem so we're able to offer comprehensive solutions for creating, deploying and managing ML models and AI-powered intelligent applications.

Explore solutions with software and hardware partners certified on Red Hat OpenShift for all your AI/ML workloads in the Red Hat Ecosystem Catalog.

Sobre el autor

Adam Wealand's experience includes marketing, social psychology, artificial intelligence, data visualization, and infusing the voice of the customer into products. Wealand joined Red Hat in July 2021 and previously worked at organizations ranging from small startups to large enterprises. He holds an MBA from Duke's Fuqua School of Business and enjoys mountain biking all around Northern California.

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