RETRIEVAL-AUGMENTED GENREATION

Agentic RAG

RAG is external information – data and content – that enables GenAI models to generate more accurate and contextually relevant outputs. It is fundamental to your success in working with generative AI.
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WHY RAG?

Context is everything

A challenge with generative AI (GenAI) is that a model will always generate a response, regardless of the quality of the data it is fed or the amount of context it is given
Precision matters

One challenge is that RAG needs to be very precise and accurate in order to solve real-world business challenges.

Preparation can take time

Many analysts estimate that enterprises spend as much as 50% of their time in building GenAI apps just doing data preparation.

Getting RAG right is a challenge

At first glance, many GenAI apps appear to produce amazing results and yet – upon further inspection – aren’t accurate enough to get to production.

Failing to deliver outputs at the level of accuracy and relevance that the business needs is why so many GenAI apps get permanently stuck in POCs
THE VERTESIA APPROACH

Our agentic RAG pipeline streamlines the process of data preparation, data retrieval, and response generation

A RAG pipeline incorporates everything you need to deliver superior results with RAG. We employ GenAI agents throughout the pipeline to automate and accelerate RAG preparation, helping to enhance the accuracy and relevancy of your GenAI outputs.
VERTESIA PROVIDES

Intelligent content pre-processing

Content pre-processing is about getting content into a form where it is efficiently and easily usable by a generative AI (GenAI) model
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Content preparation
Content has to be converted into flat text, videos need to be transcoded, and audio transcribed
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New content generation
In some cases, new content – or meta content – needs to be generated to better facilitate RAG processing
In many cases, we find that three, four, or even more steps are required to properly prepare content for RAG

Our platform uses agents to assist with content pre-processing, leveraging different GenAI models to intelligently automate this process

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Agentic processing
GenAI agents assist Vertesia users to intelligently pre-process content for RAG. These agents not only enrich content for better GenAI outcomes, they also automate your RAG pipeline.
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Patent-pending patterns
To date, Vertesia has five different patent-pending patterns for working with complex content.
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Meta content
At Vertesia, we use the concept of meta content to describe intermediate or transitory new content that is created during content pre-processing. Meta content is much easier for GenAI models to effectively and efficiently process.
VERTESIA PROVIDES

Hybrid search

We believe there is a right GenAI model for every task and there is a right retrieval method for every use case
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Hyrbid retrieval
Combine multiple search methods to further refine retrieval results. For example, refine semantic search results with structured and graph searches.
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Multiple vector indexes
The Vertesia platform creates multiple vector indexes. Full text, property, and visual image indices enhance semantic search accuracy.
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Embeddings
Embedding structures are unique to GenAI models. When you switch models, you need to reindex and utilize a different embedding. With Vertesia, this is automatic.
Some may argue that graph is better than vector or that full text may not be accurate enough. We say, why force a choice on our users?
VERTESIA PROVIDES

Semantic chunking

Vertesia's patent-pending "semantic chunking" is an agent-driven automation that intelligently chunks large documents into semantic groupings
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Semantic groupings
An agent intelligently chunks text based on its understanding of the language
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Context preservation
We prevent the separation of critical concepts or losing vital context across across arbitrary, character-based chunks
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Input limits
Vertesia automatically addresses model input limits while ensuring quality outputs

Why does this matter?

In our experience, GenAI models tend to be very unintelligent in the way that they “chunk” or break down long-form content for processing. GenAI models commonly utilize character or page counts to chunk large documents. The issue is that if a critical concept in the document bridges across two different chunks, its meaning is lost to the model and you will get erroneous responses or “hallucinations.”

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Better results

Semantic chunking reduces the risk of losing meaning across token windows, leading to more accurate responses and better comprehension.

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Prevent hallucinations

Semantic chunking ensures that search queries return more relevant and contextually complete results. This reduces hallucinations and enhances the precision of information retrieval.

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Cost optimization

Semantic chunking minimizes redundant or unnecessary text in prompts which optimizes token usage. This leads to lower processing costs and improved performance.

With Vertesia, you get the highest quality GenAI responses

ENTERPRISE ARCHITECTURE GUIDE

Effective RAG Strategies for LLM Applications & Services

This paper explores the intricacies of RAG strategies, emphasizing the superiority of semantic RAG for enterprise software architects aiming to build robust LLM-enabled applications and services.

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