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What are you searching for?
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.
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 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.
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.
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.
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.
We'll start by discussing your specific goals and desired outcomes for the LLM application.
Our data engineering team will clean, preprocess, and organize your new data sources.
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.
After that, we'll integrate your existing LLM with the RAG system.
Our NLP experts will collaborate with you to design effective prompts and instructions for the LLM.
We'll train and fine-tune the RAG system to improve the quality and accuracy of its generated text.
Our team will continually evaluate the system's outputs, ensuring they meet your requirements.
Based on this evaluation, we might refine the data sources, retrieval methods, or prompts to optimize the overall effectiveness of the RAG system.
We'll monitor system health, addressing any technical issues, and staying updated on the latest advancements in RAG technology.
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.
Our team offers extensive expertise in crafting effective prompts to guide the RAG model towards the desired outcome.
Geniusee 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.
Retrieval augmented generation (RAG) combines retrieval-based and generative models, using external knowledge sources to generate contextually relevant responses. Large Language Models (LLMs), on the other hand, rely solely on internal training data to generate LLM responses.
Retrieval augmented generation excels in handling complex queries by incorporating external context, while LLMs generate responses based solely on their learned patterns.
The RAG method for LLMs involves improving the generative capabilities of LLMs by extending the RAG mechanism.
Example: retrieval augmented generation in customer service. RAG can allow chatbots to find relevant information (like return policies) from a company's database and combine it with their knowledge to answer questions accurately.
More accurate answers: RAG verifies information with real-world sources, reducing factual errors and hallucinations (made-up info) from LLMs.
Up-to-date knowledge: RAG can access constantly updated information, unlike static LLM training data.
Increased trust: users can see the sources used to generate responses, making rag outputs more believable.
