RAG Framework in HALO

As part of our commitment to transparency and continuous improvement, we share the theoretical underpinnings of HALO's AI framework. Understanding the mechanics of HALO's Retrieval Augmented Generation (RAG) algorithm can help you better appreciate how it enhances and refines interactions within your organization. This powerful system ensures that communication is not only accurate but also contextually relevant, harnessing the latest advancements in Agentic AI.

The Role of LLMs and RAG in HALO

HALO leverages the capabilities of Large Language Models (LLMs) while mitigating their common pitfalls, such as inaccuracies and inappropriate outputs. This is achieved through the use of a Retrieval Augmented Generation (RAG) framework. The core concept behind RAG is to utilize both retrieval mechanisms and generative capabilities to optimize the performance of LLMs.

In practical terms, this means HALO retrieves pertinent information before calling an LLM to address a query. By supplying relevant source content to the LLM, HALO ensures more accurate and contextually appropriate responses.

Requirements

To effectively use HALO and take full advantage of its RAG capabilities, certain prerequisites must be met:

• Knowledge: Comprehensive knowledge should be provided by adding and synchronizing resources. This information is processed into smaller text segments, known as "chunks," and stored in an internal database. Proper knowledge management is crucial for optimal retrieval.

• Style: The desired agent behavior should be configured, including language preferences and tone of voice. This ensures that responses align with your organization's communication standards.

Approach

When a user submits a query, HALO employs the RAG algorithm through the following steps:

1. Retrieval: HALO compares the incoming query against the pre-chunked knowledge base of your organization. It identifies the three most relevant chunks and compiles them into a single block of rich context.

2. Rewriting: This context is passed to an LLM, guiding it to craft a response that adheres to the specified tone of voice and relies solely on the factual data within the context.

By integrating these sophisticated processes, HALO's RAG framework upholds the integrity and accuracy of AI-driven interactions, fostering trust and reliability in your digital communications ecosystem.