RAG In Enterprise AI: What is it & Why It's A Hot Topic?

LLMs are great. But is it for your business?

LLMs based applications can generate instant and accurate responses in most scenarios. However, they get interestingly wrong in some cases.

You as a business owner cannot risk your brand image and customer trust to LLM hallucinations. But why do LLMs hallucinate?

It’s because they don't have access to up-to-date information and reliable sources. And this is the biggest challenge for businesses looking to integrate LLMs into their operations.

Businesses hurry to adopt AI solutions in their models. But they forget to check:

·       If the LLMs are business ready?

·       Is it financially viable?

·       Are the solutions scalable and secure?

·       Will it offer quality as expected?

·       Most importantly, LLM training and implementation techniques

For generative AI to be enterprise-ready it needs to be secure, scalable, and reliable. So, you that you are sure:

·       Your data is secure with proper access controls

·       You can upgrade your AI app as and when required

·       Your AI app is reliable enough to produce consistently accurate output as intended.

How can businesses achieve all the above three? - By augmenting the LLMs with their own proprietary data. This is where RAG comes into the scene.

Let’s discuss the importance of RAG in enterprise AI solutions.

What is RAG?

RAG or Retrieval Augmented Generation is the process of optimizing the LLM's output by enabling it to refer to an external knowledge base alongside its training data.

Let’s understand it this way – LLMs are trained on large datasets which are publicly available. Also, some LLM applications can scrape off the internet to find relevant information to generate responses.

For personal use, public and internet sources can give you satisfactory results. But when we are talking about your business, LLM based apps might not produce meaningful results.

You may encounter hallucinations and end up with outdated responses with no reliable sources.

You need to equip the LLM with your business and domain data for it to cater to your specific needs. RAG further enhances the abilities of the LLMs by giving them access to your organization’s knowledge base. This enables the model to refer to your business data before generating a response, making it more relevant and reliable.

The best thing is that it’s the most cost-effective way to improve the LLM’s output without the need to retrain it from scratch.

To sum up, RAG:

·       improves the efficiency, quality, and reliability of the AI applications

·       incurs low training cost

·       brings in more transparency

·       ensures data security and credibility of AI apps

However, the effectiveness of your AI app heavily relies on your data pipelines that make quality enterprise data available to AI models.

Why RAG is important for enterprise AI?

The answer to the above question is:

-        to reduce hallucinations

-        enable LLM apps to answer meaningful business questions

-        most importantly, make AI apps enterprise ready  

Let’s assume your customer wants to cancel his order. Now the AI customer chatbot will require information about your cancellation policy, customer details, order history, and past transactions to cancel the order.

Without access to these data, the chatbot will generate a response backed by public information or internet sources. And that response will be absurd and won’t fulfill your customer needs. This may lead to poor customer experience and trust loss in your services.

This is where RAG helps the AI apps by giving them access to your business database. The app then retrieves the required information depending on the query to produce personalized responses.

However, exposing your sensitive business data is risky. RAG makes sense to enterprise generative AI applications by assuring its security, scalability, and reliability.

Here’s how:

RAG security and privacy are more manageable:

Unlike AI models, enterprise databases are more secure and manageable with standard access controls. You always have control over who can access what data.

This is RAG's advantage over finetuning which exposes the training data to all users of the app without any way to control who sees what.

RAG generates reliable and verifiable results:

Finetuning brings lots of hallucinations and inaccuracies. Plus, in case of changes in business policies, you will have to retrain the model all over again. That’s costly.

But with RAG, two things happen simultaneously for the benefit of your business. First, the LLM app generates responses backed by your reliable knowledge base. This results in quality up-to-date responses based on top of verifiable sources. Secondly, in case of any policy changes, all you need to do is to update your database.

RAG is more scalable:

RAG is easy to deploy, use, and cost-efficient. It doesn’t require labeling or crafting, which may take months to build an ideal model. Plus, unlike finetuning you wouldn’t have to deal with millions of issues for upgrading your AI app.

What LLM challenges does RAG resolve?

Challenge: Large Language Models (LLMs) lack access to your specific information.

LLMs are trained on vast amounts of public data, making them versatile for various tasks.

However, this training data has a limit, and LLMs can't access new information on their own. This can lead to inaccurate or outdated responses, especially for questions outside their training scope.

Challenge: Why does custom data matter for effective AI applications?

For businesses to get the most out of LLMs, they need models that understand their specific field and can answer questions using their own data.

Imagine a customer service bot trained on general information versus one that understands your company's policies and procedures. The latter delivers better service.

The same goes for internal Q&A systems that should utilize an organization's HR data. But how can companies achieve this without constantly retraining models?

Introducing Retrieval-Augmented Generation (RAG): The solution for leveraging your data

A simple and powerful approach is to use Retrieval-Augmented Generation (RAG). 

Here's how it works: relevant data from your organization is retrieved and fed to the LLM along with the user's query. This provides additional context for the model, allowing it to go beyond its pre-trained knowledge.

Unlike retraining, RAG allows you to integrate your data with any LLM, delivering relevant results without the time and cost involved in traditional methods.

Why Retrieval-Augmented Generation (RAG) is a game-changer for LLMs?

Fresh and Factual Responses: RAG ensures LLMs ditch outdated training data.  Instead, they leverage real-time information from external sources for accurate and up-to-date responses.

Combating Hallucinations: By grounding LLM outputs on relevant external knowledge, RAG minimizes the risk of fabricated information, also known as hallucinations.  Citations of original sources can even be included for human verification, fostering trust.

Domain Expertise on Demand: RAG empowers LLMs to deliver highly relevant responses tailored to an organization's specific data and domain. This ensures responses are not just generic but truly insightful.

Simple and Cost-Effective:  Compared to other LLM customization methods, RAG is a breeze.  Organizations can integrate RAG with any LLM without model alterations, making it ideal for scenarios where frequent data updates are needed. This translates to significant cost savings and faster implementation.

When to use RAG?

RAG is the process that enables LLM to access external data sources to respond to a query. This data source is the verifiable and reliable knowledge base of your company's data. This approach refines the quality of the output and increases its relevancy.

Therefore, RAG is best suited for AI applications that:

-        need external contextual database

-        conducts a question and answer in a specific domain

-        does querying structured and unstructured datasets

-        required to provide up-to-date information

RAG applications are designed to access external data sources to generate reliable responses. This approach makes it suitable for implementing AI across major business operations that include customer support, product recommendations, chatbots, search engines, legal compliance systems, employee onboarding, and more.

Building RAG Applications: A Step-by-Step Guide

There's no one-size-fits-all approach to implementing Retrieval Augmented Generation (RAG). The specific workflow depends on your needs and data. This guide outlines a common setup to get you started.

1. Data Preparation: Gather documents along with their metadata. Preprocess the data, including handling Personally Identifiable Information (PII) as needed. This might involve detection, filtering, redaction, or substitution. Chunk the documents into suitable lengths based on the embedding model and the downstream LLM application that will use them as context.
2. Indexing Relevant Data: Create document embeddings. Build a Vector Search index using these embeddings.
3. Retrieving Relevant Data: When a user submits a query, find relevant parts of your data based on the query. Include this retrieved text data as part of the prompt for the LLM.
4. Building LLM Applications: Develop an endpoint that combines prompt augmentation with LLM queries. Expose this endpoint to applications like Q&A chatbots through a simple REST API.

My Recommendations:

Here are some key architectural elements I would suggest for an RAG system:

Vector Database:

Some LLMs leverage vector databases for fast similarity searches, typically to provide context or domain knowledge during LLM queries.

Schedule regular updates to the vector database to ensure the deployed language model has access to the latest information.

The logic for retrieving from the vector database and injecting information into the LLM context can be packaged within the model artifact logged to MLflow using MLflow LangChain or PyFunc model flavors.

MLflow LLM Deployments or Model Serving:

If your LLM application uses a third-party LLM API, leverage MLflow LLM Deployments or Model Serving support for external models. This provides a standardized interface for routing requests from vendors like OpenAI and Anthropic.

MLflow LLM Deployments or Model Serving offers several benefits:

• Enterprise-grade API gateway
• Centralized API key management
• Ability to enforce cost controls

Model Serving:

When using a third-party LLM API in an RAG system, the LLM pipeline will make external API calls from the Model Serving endpoint to internal or third-party LLM APIs. This adds complexity, potential latency, and another layer of credential management compared to deploying a fine-tuned model directly.

The future of enterprise AI apps is in your data pipelines

To unlock the full potential of AI, it's crucial for both data and AI teams to approach LLM augmentation with meticulous attention, prioritizing security, scalability, and reliability as core considerations.

Whether your project leans towards RAG, fine-tuning, or a combination of both, establishing robust foundations within your data infrastructure is paramount to controlling costs, maintaining consistent performance, and ensuring high reliability.

Data integrity and privacy must be safeguarded, LLM deployment must be capable of scaling seamlessly, and the outcomes generated must be trustworthy.

Vigilantly monitoring data quality through observability mechanisms is indispensable to meeting these requirements.

What's particularly exciting about this shift from isolated X demos to AI solutions tailored for enterprise use is that RAG empowers data engineers to play a pivotal role in driving ROI for investments in generative AI. It grants them significant influence in decision-making and strategy formulation.

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