RAG wasn’t a concept 9 months ago. Today everybody talks about how to ‘Chat with Data’ and to do that us and others have popularized the concept of Retrieval Augmented Generation, RAG in 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 LLMs with more traditional search techniques are a few:
- This setup will reduce hallucination
- It’s a cost effective alternative to ‘just’ use an LLM (GPU compute is more expensive than CPU compute)
- LLMs are at core not meant for large scale search operations but for text comprehension nd text generation
- Domain specific data: You interact with ‘just’ your (enteprise) data
- Context aware outputs: A properly setup search engine will be able to comprehend and compute context into any answer
- You can easily build industry or use case specific accelerators
- And a few more (link to our RAG papers)
Now for enterprise usage at scale 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 entprise ready setup that includes how to operate such a RAG stack over time with often stringent requirements on how to develop, test, put into production and operate with different software enviornments, etc.
- Any enterprise operates not just on textual data (what LLMs are good at primarily) but on real-time operational data from manifold sytems.
- And in an enterprise it’s not about ‘chat’ but ‘action’ as in reliable and consistently produce an output that is desired in the market place
This elements are all missing in a ‘standard’ RAG setup.
We are announcing today the next level: RAG+++. We add a few critical components to the RAG architecture:
Enterprise Security
We have been working hard to bring a few key enterprise security requirements: Entitement handling / a full Access Control Lists (ACLs) implementation, transparent data lineage, a robust testing system. With this Squirro enhances data protection and compliance, a traceable path of data flow and transformations, ensuring accountability and integrity.
Graphs: Taxonomies and Process Graphs
The first major enhancement in 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: These illustrate the relationships and sequences between various processes or operations. In a business context, process graphs can model workflows, supply chains, or customer journeys. By incorporating process graphs, RAG+++ can provide insights and responses that consider 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 it’s 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 real data while preserving privacy and confidentiality. By using synthetic data, RAG+++ can train and operate AI models without exposing sensitive information. With this approach you test an application in lower environments and/or sandboxes. You also can expose sensitive data without any worry to 3rd party LLMs. This approach not only enhances security but also 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 are critical for any organization. RAG+++ introduces robust guardrails to ensure these aspects are not compromised. These guardrails function as predefined rules and checks that 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 in the background extended prompts 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.
- Tone of Voice: The responses maintain the intended tone, whether it’s 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+++ 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.
For a sneak peak watch these videos:
https://go.squirro.com/Agents_AM
Conclusion
RAG+++ represents a significant leap forward. By integrating graphs, real-time data ingestion, synthetic data for security, stringent guardrails, and agents RAG+++ offers a comprehensive solution that addresses the multifaceted challenges of any enterprise. Happy to chat how we can support your business.
PS:
Initially (24 months ago) we called it raLLM – retrieval augmented LLMs. RAG came around and was adopted as the moniker for this combination of retrieval and LLMs. Here’s the challenge to the community: RAG+++ is our moniker. Any better ideas?