RAG as a service
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Retrieval augmented generation as a service
If you're considering using large language models to enhance your apps or services, retrieval-augmented generation (RAG) is a great way to access new knowledge while controlling the results. Whether you want to improve search, summarize text, answer questions, or create content, RAG lets you take advantage of the benefits of advanced AI while staying in charge of its output.
In partnership with:
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Retrieval-augmented generation is a method that makes large language models (LLMs) more accurate and reliable by adding information from external sources during the generation process.
Retrieval
When a user submits a prompt to an LLM enhanced with RAG, the system retrieves relevant data from an external knowledge base to provide a more informed response.
Augmentation
This retrieved data enhances the LLM's built-in knowledge by adding extra context, helping it generate more accurate and relevant responses.
Generation
The LLM processes the user query by combining its language understanding with the additional information retrieved while preparing a response based on this enriched context.
Our RAG services
Our team can identify and prepare the external data source for the LLM and ensure that this data is up-to-date and relevant to the LLM's domain.
Our experts can design and implement a system to search and retrieve relevant information from the external data source using vector databases.
Our team can develop algorithms to analyze user queries or questions and identify the most relevant passages from the external data.
Our tech experts can develop a system that incorporates snippets from the retrieved data or keyphrases to guide the LLM's response.
We can monitor the system's performance and user feedback to continuously improve the retrieval process and LLM training data.
What's possible with RAG as a service
Extensive knowledge
Unlike standard LLMs, which are restricted to pre-trained data, RAG integrates with external knowledge bases, allowing it to access a vast pool of information.
Enhanced relevance
RAG retrieves real-time, relevant information from external sources to supplement its responses, making outputs more accurate and appropriate to user queries.
Branded content creation
Beyond answering questions, RAG supports businesses in efficiently generating personalized content such as blog posts, articles, and product descriptions.
Detailed market research
RAG can analyze current news, industry reports, and social media data to uncover trends, gauge customer sentiment, and gain insights into competitor strategies.
Building user trust
By citing sources and including references in its outputs, RAG ensures transparency, enabling users to verify the information and explore its origins.
Multilingual support
Retrieve information in one language and generate responses in another, providing multilingual capabilities for global use cases.
Real-time insights
Access and synthesize real-time information for dynamic industries like finance, healthcare, or e-commerce.
Document summarization
Automatically summarize large volumes of documents by retrieving relevant content and generating concise summaries.
The benefits of our retrieval-augmented services
Adaptability
RAG systems can be adapted to different fields by modifying the external data sources they rely on. This possibility makes it easy to launch generative AI applications in new industries without extensive retraining of the underlying language model.
Easier updates
Maintaining a RAG system is straightforward, as updating the external knowledge base is simpler than retraining a language model. The directness of this process ensures the system stays up-to-date with the latest information while reducing upkeep complexity.
Controlled data sources
With RAG, you have full control over the data sources that the system references. Unlike traditional LLMs trained on vast datasets with unknown origins, RAG lets you curate and rely on trusted, specific datasets.
Our client's success stories
Our process
We'll start by discussing your specific goals and desired outcomes for the LLM application. Then we thoroughly research and analyze existing information and knowledge base on the topic, look through various use cases, and gather as much data on your specific topic as possible at this stage.
Our data engineering team will prepare your data sources by cleaning, preprocessing, and structuring them effectively.
Next, we’ll configure a retrieval system and write custom code for specific use cases so that our RAG system can quickly search and provide relevant information to the LLM in response to its prompts and queries.
This step involves connecting your data sources and knowledge base to the RAG system.
Our AI specialist will work with you to create efficient prompts and guidelines for the LLM. This process is quite iterative, allowing us to continuously analyze results and improve prompts.
Our team will consistently assess the system's results to ensure they align with your expectations as well as improve processes as we learn new innovative methods or new cutting-edge trends emerge in the industry.
Based on the evaluation, we may adjust the data sources, retrieval techniques, or prompts to enhance the RAG system’s overall performance.
We’ll oversee the system’s performance, resolve technical issues, and stay informed about the latest developments in RAG technology.
Why work with us?
Experience
Our team has deep experience in designing precise prompts to effectively guide the RAG model toward achieving the desired results.
Data protection
Devstark ensures the protection of your sensitive data through strong security measures and strictly complies with data privacy regulations.
Personalization
We provide options to customize the retrieval augmented generation model, aligning it with your unique requirements and preferred data sources.
Lower TCO
We continuously monitor the industry for new, emerging models, approaches, and technological innovations that simplify development and lower our clients' total cost of ownership.
Build or buy software?
If you are wondering whether to buy or build software, which path is better and fits your specific needs the most? Take a look at our short summary of what the process entails for each case in the table below. If you are still not sure which option is best for you, you can read our article "Build versus buy software" and use our decision-making table to make a more informed choice.
Technology we use
OpenAI
OpenAI provides models like GPT (Generative Pre-trained Transformer), which excels in natural language understanding and generation. These models can be used to build everything from chatbots and content generators to code assistants and data analyzers. OpenAI's API is incredibly versatile, allowing easy integration into web and mobile applications.
Meta AI
MetaAI's powerful tools and frameworks include open-source contributions like a leading deep learning library - PyTorch, state-of-the-art open-source model Llama, various pre-trained models for translation, content generation, recommendation systems, and advancements in AI-driven augmented and virtual reality applications.
Anthropic
Anthropic produces tools and models, such as Claude, that specialize in natural language processing tasks like content generation, summarization, and question answering. These tasks are similar to conversational agents but optimized for safer and more controllable interactions, where outputs align with user intent while minimizing risks.
Cohere
Cohere is an AI platform that provides powerful natural language processing (NLP) models through an API. It specializes in large-scale language models for tasks like text generation, summarization, translation, and question-answering.
Google Vertex AI
Vertex AI is a fully managed, unified AI development platform for building and using generative AI. Access and utilize Vertex AI Studio, Agent Builder, and 160+ foundation models. Build generative AI apps quickly with Gemini, train, test, and tune ML models on a single platform, and accelerate development with unified data and AI.
Vercel AI SDK
The AI SDK is the TypeScript toolkit designed to help developers build AI-powered applications with React, Next.js, Vue, Svelte, Node.js, and more. AI SDK Core offers a unified API for generating text, structured objects, and tool calls with LLMs.AI SDK UI provides a set of framework-agnostic hooks for quickly building chat and generative user interface.
LlamaIndex
LlamaIndex is a popular open-source data framework for connecting private or domain-specific data with LLMs. It specializes in RAG and smart data storage and retrieval. LlamaIndex can connect data of any type - structured, unstructured, or semi-structured - to LLM, index and store the data, combine the user query and retrieved query-related data to query LLM and return a data-augmented answer.
Milvus
Milvus is an open-source vector database designed for high-dimensional data management and similarity search. It supports efficient storage, indexing, and retrieval of vectors, making it ideal for AI applications like recommendation systems, natural language processing, and image recognition.
Qdrant
Qdrant is an advanced vector search engine and database optimized for high-dimensional data. It facilitates efficient similarity search, making it ideal for AI-powered applications like recommendation systems, image recognition, and natural language processing.
Pgvector
Pgvector is an open-source PostgreSQL extension that provides efficient storage and similarity search for high-dimensional vector data. With support for indexing methods such as IVF, HNSW, and L2 distance calculations, pgvector delivers robust and scalable vector search functionality within a familiar relational database environment.
What our clients say
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