Enhance LLMs with Retrieval Augmented Generation (RAG)

by Jun 4, 2024#DigitalTransformation, #DigitalStrategy, #HomePage

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Table Of Content

  1. What is Retrieval Augmented Generation?
  2. How Does the Retrieval Augmented Generation Work?
    1. RAG Inference Process
    2. Software Components
  3. Leveraging GPU Infrastructure
  4. RAG Use Cases
  5. Benefits of Retrieval Augmented Generation (RAG)
  6. Generative AI Consultants


In the last decade, traditional businesses have been digitally transforming at an unprecedented pace. This era of digital disruption has been marked by the emergence of innovators who completely reshaped their respective industries.

For businesses that have learned to manage and operate with data efficiently (DataOps), implementing generative AI is the natural next step in their competitive journey.

Implementing these technologies promises new and more efficient products, user experience enhancements, and operational efficiency improvements–necessary to sustain competitive advantages.

However, LLMs face inherent limitations due to their reliance on static training data. This data caps their knowledge at a certain point and makes adapting to new, domain-specific information challenging

Retrieval-augmented generation (RAG) emerges as a key innovation, enabling LLMs to dynamically extend their knowledge base by referencing up-to-date, authoritative external data sources.

Companies adopting AI are developing solutions incorporating large language models (LLMs) and generative AI applications enhanced by Retrieval-Augmented Generation (RAG) capabilities.

Knowledge-intensive tasks require external data retrieval and prompts to extend the functionality of LLMs. By augmenting an LLM with domain-specific business data, organizations can craft AI applications that are both agile and adaptable to their environment.

Creating a tailored generative AI solution involves customizing or modifying existing task-specific models to align with your business objectives.

To get started, our AI engineers at Krasamo can customize and merge an off-the-shelf chat web application with data retrieval capabilities and a large language model (LLM). A general-purpose LLM enhanced with APIs for chaining domain-specific data, offering a cost-effective approach to building generative AI applications.

On this page, we explore concepts related to generative augmented generation (RAG) and knowledge sources aimed at discussing GenAI development efforts further with our business partners.


What is Retrieval Augmented Generation?

Retrieval-augmented generation is a method for enhancing LLMs by using data from external sources to increase the model’s reliability and accuracy.

LLMs work with parameterized data (without access to external data), limiting its functionality, as they can not revise, expand knowledge, or provide insight into its predictions.

They work with parameters that represent the patterns of how humans use words and form sentences (implicit knowledge). This makes them particularly fast in responding to general prompts, but they lack the specifics of domain or topic knowledge and may produce wrong answers or hallucinations.

Developing retrieval augmented generation (RAG) functionality fine-tunes or enriches the application by connecting the LLM with specific external knowledge.


How Does the Retrieval Augmented Generation Work?

A Krasamo developer can easily connect the LLM with your company datasets (enterprise knowledge base) to build an application that generates accurate responses for your business use case. This offers a simpler alternative to retraining a model. The following is a general overview of the RAG process.

RAG Inference Process

Data Ingestion: The process begins with importing documents from various external sources, such as databases, repositories, or APIs. These documents could be in PDF formats or other forms and contain knowledge that wasn’t available during the initial training of the foundational model.

Document and Query Conversion: The imported documents (pdf, files, long-form texts) and any user queries must be converted into a format that allows relevancy searches through embedding models.

Embedding Process: The core of RAG’s functionality lies in transforming textual data into numerical representations through embeddings. Embedding is a critical step that converts the document collection (knowledge library) and user-submitted queries into vectors, enabling the machine to understand and process textual information. This number representation of the text is really a vectorized representation of information. So for example a sentences like when is your birthday and “on which date you were born”, will have a vector representation that will be very close if not identical. (You can use existing task-specific models to provide embeddings for prompts and documents).

Relevance Search and Augmentation: The RAG model compares the embeddings of user queries with those in the knowledge library to find matches or similar documents. It then appends the user’s original prompt with context from documents that closely align with the user’s intent. This means that the concept represented on the question is somehow similar to the concept represented in one fragment of the document.

Semantic search is performed to retrieve contextually relevant data. It provides a higher understanding and precision that closely mirrors human-like comprehension.

Semantic search begins by interpreting the semantic meaning behind a user’s query. Unlike traditional search methods that rely heavily on matching keywords, semantic search understands the context and nuances of the query, enabling it to grasp the user’s intent more accurately.

This mapping is achieved through sophisticated algorithms that understand the semantic meaning of the query and can identify the most relevant information across a vast array of documents.

The vector database could retrieve the vectors that are closer, which happens to be the concepts that are related. This vectors are mapped to the original pieces of text. That way on the final prompt to the LLM the most relevant text is presented in the prompt. It is still a long prompt but now the information sent is distilled with the most relevant fragments of text.

Prompt Augmentation: The selected relevant documents are used to augment the original user prompt by adding this contextual information. This augmented prompt is finally passed to the foundational model for generating a response–and enhances the foundational model’s ability to generate accurate and contextually rich responses.

Continuous Update: Knowledge libraries and their embeddings can be updated asynchronously (a recurring process) to ensure the system evolves and incorporates new information.


Software Components

Building a retrieval-augmented generation (RAG)–based application requires expertise in several software components and operations with data pipelines (LLMOPs).

Some components are foundational models, frameworks for training and inferencing, optimization tools, inference serving software, vector database tools to accelerate search, model storage solutions,  and data frameworks such as LangChain (open-source) and LlamaIndex for interacting with LLMs.


Leveraging GPU Infrastructure

When developing GenAI products and choosing infrastructure providers, accounting for the significant memory and computing resources required to process and move data efficiently is crucial. Engage with your providers to ensure your infrastructure meets these demands.

Running multiple models concurrently on the same infrastructure necessitates careful planning. Implementing GPU optimization techniques can enhance GPU utilization, making your systems more efficient and cost-effective.

By incorporating GPU optimization strategies and adhering to best practices in large language model operations (LLMOps)—such as efficient data loading, caching, and employing parallel processing techniques—organizations can significantly boost the performance and scalability of LLM workloads on GPU-based infrastructures.

Moreover, integrating GPU-accelerated databases into the LLMOps pipeline enables organizations to enhance the efficiency and performance of their LLM workloads. This integration facilitates quicker model training and inference, along with more rapid data processing, maximizing the utility of the GPU infrastructure.


RAG Use Cases

  • Web-based Chatbots: Power customer interactions with a chat experience that responds to questions with insightful answers.
  • Customer Service: Improve customer service by having live service representatives answer customer questions with updated information.
  • Enterprise Document Search: Empower employees to query internal documentation search to retrieve information.
  • Tabular Data Search: AI can instantly navigate vast data sets to find insights, enhancing decision-making.


Benefits of Retrieval Augmented Generation (RAG)

It is difficult to identify which knowledge an LLM has and to update or expand its knowledge without retraining. As models must be increasingly large to store more knowledge, this becomes computationally expensive and less efficient.

By building RAG applications, we enable a model to fetch documents from a large corpus based on the context of the input and, within the pre-training process, obtain some of the following benefits:

  • Solve the limitations of implicit knowledge storage in Large Language Models regarding interpretability, modularity, and scalability.
  • Retrieving documents as part of the prediction process makes it easier to see which external knowledge the model uses to make its predictions.
  • Separating the retrieval process from the prediction model allows for updates and modifications to the knowledge source without retraining the entire model.
  • Instead of needing larger models to store more knowledge, RAG applications leverage external documents, thus potentially reducing the size and computational needs of the core model.
  • Improves performance on knowledge-intensive tasks (that require specific knowledge)


Generative AI Consultants

Some organizations operating in specific business contexts may need more resources and time to build AI capabilities and may opt for partnering with an AI consultant.

Krasamo is an AI consultancy organization that focuses on building data infrastructures, standardizing data management processes, and improving data quality to securely share data across the organization. Thanks to our work with machine learning systems and AI, our teams have expertise in building generative AI applications. Contact us for more information about our services.

About Us: Krasamo is a mobile-first digital services and consulting company focused on the Internet-of-Things and Digital Transformation.

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