Chat Is Not Enough. Unveiling The Next Level of RAG+++

RAG wasn't a concept nine months ago. Today, everybody talks about ‘Chat with Data'. To do it, you need Retrieval Augmented Generation, or RAG for short. 

The traditional RAG stack combines the strengths of information retrieval systems and generative models. In essence, it retrieves relevant documents or data points and uses this information to generate more accurate and contextually appropriate responses. 

The main drivers for this combination of Large Language Models (LLMs) with more traditional search techniques are a few: 

• This setup will reduce hallucination 
• It's a cost-effective alternative to ‘just' using an LLM (GPU computing is more expensive than CPU computing)
• LLMs are, at the core, not meant for large-scale search operations but for text comprehension and text generation
• Domain-specific data: You interact with ‘just’ your (enterprise) data
• Context-aware outputs: A properly setup search engine will be able to comprehend and compute context into any answer
• You can quickly build industry or use case-specific accelerators 
• And a few more (explore our curated list of resources on RAG below including a demo request)

But Chat Is Not Enough

In high-stakes enterprise environments, where the accuracy and reliability of information are critical, RAG's ability to retrieve accurate data from trusted sources and present it coherently ensures that businesses can trust the output. This trust is essential for informed decision-making processes.

For enterprise usage at scale, though, a few elements are missing:

• Enterprise security, including entitlement control (who is allowed to see what), data lineage, and governance (who does what with data) 
• An enterprise-ready setup that includes operating such a RAG stack over time, with often stringent requirements on developing, testing, putting into production, and operating with different software environments, etc. 
• Any enterprise operates not just on textual data (what LLMs are good at primarily) but on real-time operational data from manifold systems. 
• In an enterprise, it's not about 'chat' but 'action,' as in reliable and consistent production of a desired output in the marketplace.

These elements are all missing in a 'standard' RAG setup. 

Enterprise-Ready RAG: Introducing RAG+++ 

With over 10 years of experience, Squirro pioneered enterprise-ready Retrieval-Augmented Generative AI (RAG) solutions. Today, we are announcing the next level: RAG+++.

We add a few critical components to the RAG +++ architecture:  

Enterprise Security

We have been working hard to implement a few key enterprise security requirements: entitlement handling/full Access Control Lists (ACLs) implementation, transparent data lineage, and a robust testing system. With this, Squirro enhances data protection and compliance, a traceable path of data flow and transformations, and accountability and integrity.

Graphs: Taxonomies and Process Graphs

The first significant enhancement to the RAG+++ is the incorporation of graphs, specifically taxonomies and process graphs. 

• Taxonomies: These hierarchical structures classify information in a way that reflects the relationships between different data points. By integrating taxonomies, RAG+++ can better understand the context and nuances of the information it processes. This leads to more accurate and relevant data retrieval and generation.
  

• Process Graphs illustrate the relationships and sequences between various processes or operations. Process graphs can model workflows, supply chains, or customer journeys in a business context. By incorporating process graphs, RAG+++ can provide insights and responses considering the entire operational context, leading to more informed and strategic decision-making.

Real-Time Operational Data Ingestion

Another significant advancement in RAG+++ is the ability to ingest and operate with real-time operational data sets. This capability ensures that the system is always up-to-date with the latest information, which is crucial for applications that rely on timely and accurate data.

Whether monitoring live sensor data in an industrial setting or tracking real-time market trends, RAG+++ can process and utilize this data to provide relevant insights and predictions.

Enhanced Security With Synthetic Data

Data security is a paramount concern in today's digital age. RAG+++ addresses this by incorporating the use of synthetic data. Synthetic data is artificially generated data that mimics accurate data while preserving privacy and confidentiality. RAG+++ can train and operate AI models using synthetic data without exposing sensitive information.

With this approach, you test an application in lower environments and sandboxes. You also can expose sensitive data without any worry to 3rd party LLMs. This approach enhances security and ensures compliance with data protection regulations.

Guardrails for Prompts, Brand, Regulatory, and Tone of Voice Compliance

Maintaining consistency in brand messaging, adhering to regulatory requirements, and preserving the intended tone of voice is critical for any organization.

RAG+++ introduces robust guardrails to ensure these aspects are not compromised. These guardrails function as predefined rules and checks the system adheres to during data processing and response generation.

They ensure that:

• Better Prompting: Most of us are, at best, okay with formulating complex prompts even though they are required for good answers. The system provides extended prompts in the background for better results.
• Brand Consistency: The generated content aligns with the organization's branding guidelines, including visual style, messaging, and overall identity.
• Regulatory Compliance: The system operates within the legal and regulatory frameworks applicable to the industry, avoiding potential legal pitfalls.
• The Tone of Voice: The responses maintain the intended tone, whether formal, friendly, authoritative, or casual, ensuring consistent communication with the audience.
  

Agents to Autonomize Workflows

Agents complement the RAG+++ structure by enhancing its capability to handle diverse and complex tasks. While RAG+++ combines information retrieval and generation to produce accurate and relevant responses, agents can dynamically manage, orchestrate, and optimize these processes. This synergy improves the efficiency, accuracy, and adaptability of the RAG system in various applications.

The introduction of RAG+++ as a comprehensive solution holds transformative potential across various industries:

• Finance: Ingesting real-time market data, understanding financial taxonomies, and ensuring compliance with regulatory frameworks can lead to better investment strategies and risk management.
• Healthcare: Real-time patient data, taxonomies of medical conditions, and process graphs of treatment protocols can enhance patient care and operational efficiency.
• Manufacturing: Monitoring real-time production data, mapping out process graphs of manufacturing workflows, and maintaining data security can optimize operations and improve product quality.
• And similar impact in any other industry.

Cost-Effective and Scalable Enterprise Solution

To summarize, RAG+++ provides a more cost-effective and scalable approach for enterprises than techniques like fine-tuning large LLMs from scratch. It is fortified with robust enterprise-grade features and capabilities that ensure security and reliability for enterprise-level deployment.

Squirro's cross-industry-tested and mature RAG+++ offering empowers businesses to unlock the full potential of their proprietary data. It enables seamless "chat with organizational data" capabilities, allowing clients to leverage their unique knowledge assets like never before. By integrating graphs, real-time data ingestion, synthetic data for security, stringent guardrails, and agents,  this is a comprehensive solution that addresses the multifaceted challenges of any enterprise. Here are some of our best resources for enterprise-ready solutions: 

• This whitepaper offers a comprehensive exploration of the RAG concept and GenAI capabilities in the enterprise: Discover why you need Retrieval Augmented Generation in 2024
• Another blog for the C-Suite tailored for executives and leaders spearheading organizational strategy and digital transformation initiatives, including CEOs, CTOs, and CMOs.
  

Curious? Book a Demo

Experience the future of enterprise AI firsthand - request a personalized demo of our best-in-class RAG offering today. Our experts will guide you through a personal demonstration tailored to your industry, showcasing how Squirro can transform how you access and utilize your organization's data assets.

Tap into our expertise and leverage our client success cases. Reach out here to get started: