RAG as a service
You may be considering leveraging large language models to improve your applications and services. Retrieval augmented generation presents an opportunity to tap into the new pool of knowledge while maintaining control over outputs. Whether you are looking to improve search, summarize documents, answer questions, or generate content, RAG as a service can help you get advanced AI while retaining oversight.
What is retrieval augmented generation?
Retrieval augmented generation (RAG) is a technique that helps improve the accuracy and reliability of large language models (LLMs) by incorporating information from external sources.
When a user provides a prompt to an LLM with RAG capabilities, the system searches for relevant information in an external knowledge base.
This retrieved information is used to supplement the LLM's internal knowledge. Basically, it's giving the LLM additional context to work with.
Finally, the LLM uses its understanding of language and the augmented information to generate a response to the user query.
Our retrieval augmented generation services
Data preparation
Our team can identify and prepare the external data source for the LLM and ensure that this data is relevant to the LLM's domain and up-to-date.
Building the information retrieval system
Our experts can design and implement a system to search and retrieve relevant information from the external data source using vector databases.
Creating an information retrieval algorithm
Our team can develop algorithms to analyze user queries or questions and identify the most relevant passages from the external data.
LLM prompt augmentation
Our tech experts can develop a system that incorporates snippets from the retrieved data or keyphrases to guide the LLM's response.
Evaluation and improvement
We can monitor the system's performance and user feedback to continuously improve the retrieval process and LLM training data.
Capabilities of RAG as a service
Unlike traditional LLMs limited to their training data, RAG can access a vast amount of information from a knowledge base
Rag as a service retrieves up-to-date information related to the prompt and uses it to craft a response, resulting in outputs that are more accurate and directly address the user's query.
RAG's abilities extend beyond answering questions. It can assist businesses in content creation tasks like crafting blog posts, articles, or product descriptions.
It can analyze real-time news, industry reports, and social media content to identify trends, understand customer sentiment and gain insights into competitor strategies.
RAG allows the LLM to present information with transparency by attributing sources. The output can include citations or references, enabling users to verify the information and delve deeper if needed.
The benefits of our retrieval-augmented services
RAG systems can be easily adapted to different domains by simply adjusting the external data sources. This allows for the rapid deployment of generative AI solutions in new areas without extensive LLM retraining.
Updating the knowledge base in a RAG system is typically easier than retraining an LLM. This simplifies maintenance and ensures the system remains current with the latest information.
Unlike LLMs trained on massive datasets of unknown origin, RAG implementation allows you to choose the data sources the LLM uses.
Our work process
Assessment
We'll start by discussing your specific goals and desired outcomes for the LLM application.
Data gathering and prompt engineering
Our data engineering team will clean, preprocess, and organize your new data sources.
Retrieval system setup
Then, we'll set up a retrieval system that can efficiently search and deliver relevant information to the LLM based on its prompts and queries.
LLM integration
After that, we'll integrate your existing LLM with the RAG system.
Prompt design
Our NLP experts will collaborate with you to design effective prompts and instructions for the LLM.
Training
We'll train and fine-tune the RAG system to improve the quality and accuracy of its generated text.
Evaluation
Our team will continually evaluate the system's outputs, ensuring they meet your requirements.
Refinement
Based on this evaluation, we might refine the data sources, retrieval methods, or prompts to optimize the overall effectiveness of the RAG system.
Ongoing support
We'll monitor system health, addressing any technical issues, and staying updated on the latest advancements in RAG technology.
RAG applications for different industries
RAG models can analyze a user's financial data, such as bills (with consent), and recommend suitable investment options, loan products, bills, or budgeting strategies.
Retrieval augmented generation can personalize learning experiences by tailoring relevant content to a student's strengths, weaknesses, and learning pace.
RAG can be used to create unique and informative product descriptions that go beyond basic specifications.
Retrieval augmented generation can be used to create virtual tours of properties or to analyze market trends and property data to generate automated valuation reports.
Why choose us?
Our team offers extensive expertise in crafting effective prompts to guide the RAG model towards the desired outcome.
Standupcode has robust data security practices in place to protect your sensitive information and adheres to data privacy regulations.
We offer customization options to tailor the retrieval augmented generation model to your specific needs and data sources.
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