RAG

What is RAG (Retrieval-Augmented Generation) ?

RAG (Retrieval-Augmented Generation) is a technology that enhances the accuracy of responses by combining text generation from large language models (LLMs) with external information retrieval. In Japanese, it is also referred to as "検索拡張生成" (search-augmented generation) or "取得拡張生成" (retrieval-augmented generation). Meta Akita utilizes KnowledgeGraph technology to achieve the desired AI Chat bot.

Benefits of RAG

1. Information Updateability:By leveraging external information retrieval, RAG enables the output of large language models to be updated with the latest information.

2. Improved Accuracy and Reliability:：By generating responses based on search results, the basis for the output becomes clear, reducing the risk of producing hallucinated information. Additionally, it allows for the use of internal, non-public data (such as internal policies, contracts, and manuals) to create responses using generative AI.

Architecture

RAG consists of the following two-stage process:

1. Retrieval Phase:
Searches for information related to the user's query from databases and documents.

2. Generation Phase:
Combines information obtained in the retrieval phase with the user's query, inputs it into a large language model, and generates text.

Importance of the Retrieval Phase:
Careful planning of the retrieval phase is crucial for improving the accuracy of RAG's responses. Design considerations include formatting external information and selecting search methods such as keyword or vector searches.

Use Cases of RAG

RAG can be utilized across various business sectors. Here are some examples:

1. Customer Support
Developing chatbots or operator support tools using RAG, leveraging internally accumulated FAQs and administrative manuals, reduces operator search time and enhances response quality.

2. Healthcare
Building RAG with accumulated information on medicines (efficacy, usage, dosage) enables simplified natural language searches for drug information.

3. Automatic Creation of Proposals/Plans/Requests for Approval
Building RAG with accumulated past proposals/plans/requests for approval enables precise document searches and automatic proposal creation based on past performance.

4. Legal Affairs
Accumulating legal documents and past precedents enables the automatic creation of documents for legal consultations.

*These are proposed use cases utilizing RAG, and to effectively use generative AI in business, prompt design to prevent hallucination and fine-tuning for response accuracy are necessary.

Contact Information

Meta Akita Inc. provides consulting and solution implementation support from the upstream to the downstream.

  1. Identify customer needs and set the goals
  2. Visualize the current state of the customer
  3. Analyze issues
  4. Design solutions
  5. Develop solutions
  6. Implement applications/operational education

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