How to Train AI: A Guide to Enterprise AI Strategy

Adding generative AI to enterprise apps often reveals a major gap - context poverty.

While AIs are trained on massive datasets, they typically don’t include up-to-date details about a company’s products, customers, or other critical info required for relevant responses.

It's like having a brilliant intern who's read every book but hasn't spent a day in your actual workplace. Impressive knowledge, zero real-world context.

Training AI for Enterprise Capabilities: Step-By-Step Guide With Real Estate Use Case

Knowing how to train AI systems helps companies to solve complex enterprise challenges. That’s where Retrieval Augmented Generation (RAG) and the ReAct models are essential. These are the translation layers that bridge this knowledge gap.

Let’s explore them in more detail.

How Do Rag and ReAct Work Together?

The integration of artificial intelligence in enterprise is definitely transformative for all the business domains. This viral trend brings efficiency and innovation to the table.

RAG (Retrieval-Augmented Generation) improves AI capabilities and AI training process by connecting it to company software and providing it with relevant information. It uses a vector index to search databases and APIs, finding files that best match the user’s request.

What Are RAG Advantages?

• no AI training process is required, so it’s cheap
• the data is fetched from the database only when you ask so the provided information is always up to date
• it can show retrieved documents, so it’s trustworthy

However, the downside is that RAG requires a more complex infrastructure to operate effectively.

The ReAct Paradigm offers a broader approach. Therein, AI serves not as a static text generator but as an interactive and decision-making subject.

How To Integrate RAG and ReAct Paradigms Into Your LLM?

The use of artificial intelligence for enterprise and RAG and ReAct paradigms works for better productivity and smarter solutions.

Key steps:

• Data integration and API access
• ReAct workflow setup
• RAG integration for real-time retrieval
• LLM querying and fine-tuning
• Automation agents
• User interface
• Security measures

1. Data Integration & APIs

Your LLM needs real-time access to both internal and external data sources.

• Internal Data Access:

Develop APIs that allow the LLM to retrieve data from your system's internal databases (e.g., user information, logs, history).

Ensure the LLM can interact with systems like scheduling, logs, and customer data through APIs such as REST or GraphQL.

Example tools: FastAPI, Flask, or Express.js for backend API development.

• External Data Access:

In case you want to connect your LLM with external services, set up APIs to fetch real-time data. With OAuth or API keys, you’ll be able to make authentication secure.

Example APIs: Google Calendar API, external CRMs, or other third-party services.

2. LLM and ReAct Integration

The LLM should help make well-structured decisions and take actions based on the data it retrieves.

• Frameworks:

Use tools like LangChain or Haystack to create workflows that let the LLM connect with data sources.

Implement reasoning flows where the LLM decides what data it needs and then acts based on that data.

• Predefined Task Flows:

Tailor task flows to your use cases, such as checking data history, finding resources, or scheduling actions.

Create clear prompts to guide the LLM’s thinking, helping it tackle multi-step tasks step-by-step.

3. RAG Integration

Use RAG to give the LLM access to real-time, accurate information, helping it better understand the context.

• Knowledge Base:

Set up a central repository or link the system to external data sources for storing important documents and records. Tools like Pinecone, Weaviate, or FAISS can help with storing and quickly retrieving relevant information.

• Real-Time Retrieval:

Set up APIs or systems to fetch the latest data when needed—like checking real-time availability, accessing external records, or pulling documents from knowledge bases.

• LLM Querying:

Train the LLM to recognize when it needs to fetch information during a task. Tools like OpenAI’s API or Hugging Face Transformers can make its interaction with retrieval systems seamless.

4. Tooling for Interaction

Establish seamless communication between the LLM and other system components:

• LangChain:

This tool helps structure workflows where the LLM queries data sources.

Ideal for handling multi-step tasks like checking history, pulling data, and executing actions.

• Agents for Automation:

Create automated scripts or agents that the LLM can call to perform specific actions, like scheduling or sending notifications.

Implement task schedulers like Celery or Django-Q to automate certain processes.

5. User Interface Integration

Create a user-friendly interface that makes it easy to interact with the system.

• Chatbots or Voice Assistants:

Embed the LLM into a chatbot or voice assistant for natural interaction with users.

Example tools: Dialogflow, Rasa.

• Web or App Interfaces:

Work on user-friendliness of your interfaces so that users can interact with the LLM. Use front-end frameworks (ReactJS or Angular), and backend services (Node.js or Django).

6. Security and Compliance

• Data encryption: Secure data transfers between the LLM and other systems with encryption methods like SSL/TLS.
• Authentication: Protect access using protocols like OAuth2 or JWT.
• Compliance: Apply GDPR compliance measures if you’re handling sensitive data.

Use Case of LLM Adapted to RAG and ReAct Models

AI for the enterprise, powered by ReAct and RAG, improves all the processes by combining real-time data retrieval with advanced reasoning. Discover how to train AI effectively for enterprise capabilities, using a step-by-step approach illustrated through a real estate use case.

Scenario: Tenant Maintenance Request Handling

See how easy it is to understand how to train a AI model for real-world use in real estate. Imagine a property manager using a property management system with an LLM that supports the RAG and ReAct paradigm.

The task may sound like: "Can you help me schedule a repair for the broken air conditioner in Unit 204?"

How ReAct Paradigm Works Here?

Reasoning:

The LLM identifies the key information needed to handle the situation:

• Repair history of Unit 204
• Availability of maintenance staff or contractors for air conditioner repairs
• Tenant's preferred times for scheduling the repair
• Warranty or service agreement status for the air conditioner

Action:

• Retrieve Maintenance History: The LLM looks up past records for Unit 204 to check if this is a recurring issue.
• Check Availability: It checks who’s available—either internal maintenance staff or HVAC contractors.
• Confirm Warranty Status: The LLM verifies if the air conditioner is still under warranty or covered by a service agreement.
• Schedule Repair: Based on the tenant's availability, it schedules the repair and sends confirmation to both the tenant and maintenance staff.
• Notify Tenant: The LLM sends the tenant a confirmation of the scheduled repair and informs them of any potential costs if the warranty doesn't cover it.

How RAG Works Here?

Improved retrieval of detailed maintenance data:

The LLM can use RAG to pull not only Unit 204’s recent repair history from the PMS but also older records from external or historical databases, including service logs for the whole building or past air conditioning problems.

It can also check tenant preferences, like whether they prefer morning or afternoon appointments, stored in a CRM system or even in email conversations.

Real-time data on technician availability:

The LLM can use RAG to get real-time updates on the availability of maintenance staff or HVAC contractors. This approach ensures accurate scheduling and even checks each contractor's expertise, such as their specialization in air conditioning repairs. If internal staff aren’t available, RAG can search external contractor databases to find the best options based on availability, cost, and reviews.

Warranty and service agreement retrieval:

The LLM can check the manufacturer or service provider’s database to see if the air conditioner in Unit 204 is still under warranty and if the repair is covered.

If the warranty details are stored in an external system (like a vendor’s platform), RAG will pull that info to make sure the tenant isn’t charged for repairs that are covered.

Cost comparison and recommendations:

The LLM can retrieve up-to-date pricing data from external HVAC contractors, comparing rates and suggesting the most cost-effective option for the repair.

It can also provide the tenant with cost estimates for the repair if it isn’t covered under warranty, helping them make an informed decision.

Better communication and notifications:

The LLM pulls communication preferences from tenant profiles, like email or SMS, and creates personalized messages. If the tenant has asked about repair procedures or fees before, RAG can include those details in the notification.

It can also use templates from the company’s knowledge base to send more detailed messages, like explaining the repair process, next steps, or tips (e.g., making sure someone is home at the scheduled time).

Summarised Workflow With ReAct + RAG

AI for the enterprise with ReAct and RAG helps businesses combine real-time data with smart reasoning.

1. ReAct - Reasoning

The LLM understands that it needs to schedule a repair for Unit 204’s air conditioner and collect all the necessary information before moving forward.

2. RAG - Retrieval

The LLM retrieves:

• Maintenance history of Unit 204 from internal databases
• Tenant’s availability and preferences from the CRM
• Warranty information from the air conditioner’s manufacturer or service provider
• Real-time technician availability from scheduling systems or external contractor databases

3. ReAct - Action

The LLM acts by checking for available maintenance slots and coordinating with internal staff or external HVAC contractors. It schedules the appointment based on the tenant’s preferences and sends a notification to both the tenant and the maintenance team.

4. RAG - Retrieval

Before confirming, it pulls the latest contractor rates and compares options, suggesting the most cost-effective provider if internal staff are unavailable.  

5. ReAct - Action

The LLM completes the booking and sends personalized notifications to the tenant, including cost details and service instructions.

By following these workflows, you can confidently train an AI system that meets your enterprise goals and drives innovation.

Conclusion

Developing a strong enterprise AI strategy is crucial for businesses to use the full power of AI. Adding generative AI to your enterprise apps helps your users perform complex tasks. RAG and ReAct models bring real-time, business-specific data from your company context.

These tools make AI responses more relevant and actionable. At Axon, we specialize in building AI that truly gets your business. Let’s talk about how we can make your LLM smarter.