➡️RAGs - Extending AI context
RAGs, or Retrieval-Augmented Generation systems, are an advanced AI technique that combines a language model’s generative capabilities with external data retrieval. This approach is particularly useful for making responses more accurate and grounded in specific, up-to-date, or domain-specific knowledge rather than solely relying on the model's pre-existing training. Here’s a breakdown of how RAGs work and their role in Katara AI:
What Are RAGs?
RAGs blend two main AI components:
Retrieval: When asked a question or given a task, the system first searches through an external dataset (a "corpus") to find relevant information. This dataset can be specialized or regularly updated, giving the AI access to more recent and specific knowledge than what it was originally trained on.
Generation: Once relevant documents or data points are retrieved, the generative language model processes this retrieved information to produce a coherent, accurate response. This allows the AI to base its answers on current, relevant information rather than “hallucinating” answers from its general language training alone.
How RAGs Work in Katara AI
In Katara AI, RAGs are the backbone that powers specialized agents. Here’s how they contribute to the workflows within Katara:
Tailored Information Retrieval: Katara AI relies on custom corpuses—essentially collections of domain-specific documents or data. When a user asks a question or initiates a task, the RAG system first searches these corpuses to retrieve the most relevant data. This step allows Katara AI agents to access up-to-date, highly relevant information tailored to the product’s domain, increasing response accuracy.
Informed Response Generation: After retrieving pertinent information, Katara’s base LLM model uses this data to generate a response. The result is a more precise and reliable output that’s rooted in context, making the responses far more accurate and trustworthy than what the model alone could provide.
Workflow Optimization: Each agent in Katara AI, designed to handle specific tasks, can operate with refined knowledge pulled from RAGs. This allows for the execution of workflows that are informed by both general AI capabilities and precise, on-demand data retrieval, creating an efficient and specialized approach to complex workflows.
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