Versatility of RAG Applications

RAG, or Retrieval Augmented Generation, stands out as one of the leading architectures for developing Generative AI applications. Crafting a RAG application involves constructing a RAG pipeline, which has evolved into various iterations tailored to specific use cases.

Presently, we observe three primary categories within the realm of RAG applications:

1. RAG for Reading Comprehension:

Revolves around empowering humans to streamline their tasks efficiently. Key scenarios include:

  • Frontline service advisors streamlining customer queries by swiftly accessing relevant information with RAG’s assistance.
  • Customer support agents navigating through intricate manuals, often resorting to multiple sources, to effectively address customer inquiries.
  • Retail store employees guiding customers through an array of service options, aiding in decision-making and customization.
  • Content discovery facilitated by account executives utilizing natural language search tools to access pertinent product collateral, spanning whitepapers, blogs, wikis, videos, and more.

2. RAG for Code Generation:

Focuses on augmenting developers’ coding endeavors across diverse programming languages. Notable examples include:

  • Microsoft’s Co-pilot, a widely recognized tool supporting developers in coding tasks.
  • AWS/MongoDB’s Co-pilot, proficient in generating code for manipulating MongoDB data.
  • Couchbase Capella IQ, another prominent Co-pilot, enhancing coding experiences within its ecosystem.

3. RAG for Conversational Data Interaction:

Showcases the utilization of Natural Language Query (NLQ) to interact with datastores, such as:

  • Relational databases like PostGres, MySQL, and SQL Server.
  • Data warehouses including Snowflake and BigQuery.
  • Data lakes such as S3, GCS, and Azure Blob Storage.

In this scenario, the application interprets natural language queries from users and provides responses in either tabular data format or through natural language, accompanied by the corresponding SQL query.

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