Utility Specific LLMs : Fine-Tuning/RAG/RAFT

T&D Use Cases:Transformer Overheating at Sub. Adding DER on feeder. Outages.

1. Fine-Tuning vs. RAG: Utility in Context

The integration of AI into utility operations is no longer a futuristic concept but a present necessity. In the pursuit of tailored AI solutions, two primary approaches dominate: Fine-Tuning and Retrieval-Augmented Generation (RAG). While Fine-Tuning has been a traditional method, RAG offers a complementary strategy, each with distinct advantages and use cases. Additionally, their combination—Retrieval-Augmented Fine-Tuning (RAFT)—offers a hybrid approach, merging the benefits of both.

https://developer.ibm.com/articles/awb-rag-vs-fine-tuning/

Fine-Tuning: Specialized Knowledge Adaptation

Fine-Tuning involves taking a pre-trained Large Language Model (LLM) and further training it on domain-specific data. This process adjusts the model’s internal weights to improve its performance in a specialized area. In utilities, Fine-Tuning helps models understand technical terminology, historical maintenance patterns, and operational nuances.

https://learn.microsoft.com/en-us/windows/ai/fine-tuning

Retrieval-Augmented Generation (RAG): Contextual Intelligence

RAG enhances AI capabilities by integrating external databases and real-time information retrieval instead of relying solely on pre-trained knowledge. This is crucial for dealing with dynamic data, such as grid performance, transformer health monitoring, and predictive maintenance insights. Unlike Fine-Tuning, RAG allows AI to access the latest information, ensuring more relevant and updated responses.

****How to Build a Retrieval-Augmented Generation (RAG) Application : See towards end.

https://aws.amazon.com/what-is/retrieval-augmented-generation/#:~:text=Augment%20the%20LLM%20prompt,relevant%20retrieved%20data%20in%20context.

RAFT: Bridging the Gap

A hybrid approach called Retrieval-Augmented Fine-Tuning (RAFT) blends both methodologies to maximize accuracy and adaptability. Fine-tuned models establish deep domain expertise, while RAG supplements this with real-time or external knowledge, making AI solutions more robust and practical for real-world utility applications.

https://www.superannotate.com/blog/raft-retrieval-augmented-fine-tuning#:~:text=RAFT%20combines%20RAG%20and%20fine,open%2Dbook'%20domain%20setting.

Key Differences:

• Update Frequency: Fine-tuning produces a model with static knowledge, fixed at the time of training. RAG, conversely, accesses dynamic, up-to-date information.

https://www.teneo.ai/blog/avoid-outdated-information-in-your-rag-pipeline

• Source Transparency: RAG provides clear source citations, enhancing traceability. Fine-tuned models do not offer this capability.
• Data Access: It’s often assumed that fine-tuning equips an AI with the ability to access proprietary information, but this is a separate capability achieved through retrieval-based methods.

2. Cost and Time Considerations

Both Fine-Tuning and RAG come with distinct cost and time profiles, influencing their adoption in utility settings.

• Fine-Tuning: Resource-intensive, requiring significant computational power and time.
• RAG: Less computationally demanding upfront, focusing on knowledge base curation and integration.
• RAFT: Combines the upfront investment of Fine-Tuning with the ongoing maintenance of RAG.

3. Use Cases in Utilities: Transmission & Distribution

The utility sector offers fertile ground for applying Fine-Tuning, RAG, and RAFT.

• Fine-Tuning: Predict equipment malfunctions in distribution networks using historical grid failure data.
• RAG: Detect transformer overheating in transmission systems using real-time sensor data and maintenance logs.
• RAFT: Optimize load balancing across a regional grid using historical data and real-time inputs.

4. Roles and Responsibilities in Developing Utility AI Solutions

Building effective models requires collaboration, with Subject Matter Experts (SMEs) playing a pivotal role.

• SMEs: Define operational needs, curate data, design knowledge systems, and ensure alignment and validation.
• Data Engineers: Develop data pipelines, transform data, and integrate data sources.
• Software Developers: Build applications, integrate APIs, and design user interfaces.
• AI/Machine Learning Specialists: Train models, develop algorithms, and evaluate performance.

The success of these solutions hinges on the interplay between these roles, with SMEs providing essential domain knowledge.

5. Conclusion: Navigating the Energy Transition

The utility sector's digital transformation necessitates the adoption of advanced AI techniques. As the energy landscape evolves, Fine-Tuning, RAG, and RAFT will drive efficiency and innovation. Utilities must integrate these approaches within the next 3–5 years to maintain grid resilience and sustainability.

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Build a Retrieval-Augmented Generation (RAG) Application

Problem Statement

Traditional AI models, including Large Language Models (LLMs), generate responses based on pre-trained data but lack real-time access to external or domain-specific knowledge. This leads to outdated or inaccurate responses, especially in specialized fields like asset management for electrical utilities.

Retrieval-Augmented Generation (RAG) enhances AI by allowing it to fetch and integrate external knowledge into responses dynamically. This makes it useful for applications requiring up-to-date and accurate information.

1. Define the Use Case & Data Source :

Why West Side transformer is overheating ? Data Sources: Maintenance Logs, Sensors Data

2. Choose the Right Technology Stack:

LLM, Vector Database, Retrieval Pipeline, Frontend > i,e. Claude, Weviate, LangChain, React

3. Preprocess and Store Documents in a Vector Database

Document Chunking, Text Embeddings, Store in a database

4. Implement Retrieval Pipeline

Convert quesry into embedding, Retrieve relevant documents, Feed retrieved content into LLM.

5. Generate Responses Using LLM

Once relevant data is retrieved, pass into the LLM as context, improving its response quality.

6. Optimize Performance

Rank retrieved docs and do hybrid search.

7. Deploy the RAG App

Deploy RAG pipeline using API Backend, Frontend UI and hosting on the cloud.

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2025 Power and Utilities Industry Outlook

Utilities are navigating a new era of growth and transformation as they address emerging challenges and rising demand

https://www2.deloitte.com/us/en/insights/industry/power-and-utilities/power-and-utilities-industry-outlook.html

Growth in global electricity demand is set to accelerate in the coming years as power-hungry sectors expand

https://www.iea.org/news/growth-in-global-electricity-demand-is-set-to-accelerate-in-the-coming-years-as-power-hungry-sectors-expand

Previous Posts : Shailesh Jain

https://www.dhirubhai.net/in/sjain-smartgrid/recent-activity/all/