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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.

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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.

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    When a user provides a prompt to an LLM with RAG capabilities, the system searches for relevant information in an external knowledge base.

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    This retrieved information is used to supplement the LLM's internal knowledge. Basically, it's giving the LLM additional context to work with.

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    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

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Our cases

Capabilities of RAG as a service

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    Access to extensive knowledge

    Unlike traditional LLMs limited to their training data, RAG can access a vast amount of information from a knowledge base

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    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.

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    Content generation

    RAG's abilities extend beyond answering questions. It can assist businesses in content creation tasks like crafting blog posts, articles, or product descriptions. 

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    Market research

    It can analyze real-time news, industry reports, and social media content to identify trends, understand customer sentiment and gain insights into competitor strategies.

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    User trust

    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.

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The benefits of our retrieval-augmented services

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    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.

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    Simpler system maintenance

    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.

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    Control over knowledge sources

    Unlike LLMs trained on massive datasets of unknown origin, RAG implementation allows you to choose the data sources the LLM uses.

Our work process

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Our tech stack

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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.

What our clients say about us


Why choose us?

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    Our team offers extensive expertise in crafting effective prompts to guide the RAG model towards the desired outcome.

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    Data security

    Geniusee has robust data security practices in place to protect your sensitive information and adheres to data privacy regulations.

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    We offer customization options to tailor the retrieval augmented generation model to your specific needs and data sources.


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What is the difference between RAG and LLM?

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.

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