Building Energy-domain-specific GenAI Models that Reason like Experts

How Expert-Level Domain-Specific Models will Revolutionize the Energy Sector
The energy sector is a complex landscape of technologies, infrastructure, and competing priorities - energy security, economic viability, and regulatory compliance - must be carefully balanced. As the industry transitions to decentralized and diversified energy grids, it faces critical challenges: maximizing production efficiency, accurately forecasting demand, and enhancing grid resilience. Generative AI (GenAI) offers a transformative solution, however, it needs to tailored to the energy sector’s unique demands.

At Articul8, we’re pioneering domain-specific models (DSMs) designed to reason and act like seasoned energy experts, in partnership with Electric Power Research Institute (EPRI). By training domain-specific large language models (LLMs) and visual language models (VLMs) on specialized energy-focused datasets from EPRI – spanning technical documentation, operational data, and regulatory texts, our DSMs deliver insights with unmatched accuracy and relevance. These models don’t just process information; they analyze, predict, and recommend, empowering energy professionals to make smarter decisions, cut costs, and stay ahead in a dynamic market.

Why General-Purpose Models Fall Short
General purpose LLMs and VLMs, while powerful, often stumble in the energy sector’s specialized domain. Lacking deep domain knowledge, they struggle with the nuanced terminology, intricate market dynamics, and regulatory complexities that define the industry. This can lead to vague or inaccurate outputs, undermining decision-making. As an illustration, the table below compares the answers given by A8-Energy DSM and Llama 3.3 70B to a highly technical and focused question. In this example, the general-purpose model misinterprets or oversimplifies the technical term “Coal Combustion Products (CCP)”, missing critical sector-specific implications. In contrast, the A8-Energy DSM recognizes CCP’s significance as an energy-related acronym, reasons through its applications, and delivers a precise, contextually rich response – proving its value as a trusted companion for energy professionals.

By embedding expert-level reasoning into LLMs, A8’s DSMs are set to redefine how the energy sector innovates, operates, and thrives. 

Building Energy-specific Expert DSMs: Powering the Next-Generation Platform for Energy

To overcome the shortcomings of general-purpose models, domain-specific models (DSMs) are essential for delivering precise, context-aware insights tailored to the energy sector. Drawing from our experience developing DSMs for Energy, Semiconductor, Aerospace, and other sectors, we’ve identified two critical pillars for achieving expert-level performance: 1) high-quality training data and 2) deep human expertise. This section explores how these elements drive the next generation of GenAI-powered platforms for energy.

The power of specialized training data

For a DSM to reason like an energy expert, it must be grounded in data that mirrors the breadth and depth an expert would access – given unlimited time. Open-domain datasets and textbooks alone are insufficient. Our work across Energy, Semiconductor, and Aerospace, and other sectors shows that the most effective DSMs integrate proprietary, domain-specific data, such as operational logs, internal reports, copyrighted research, and enterprise-specific records. This rich data fuels models that not only understand the sector’s nuances but also maximize Return-on-Investment (ROI) through efficient, contextually relevant outputs for enterprise applications.

Our approach at Articul8 begins with curating comprehensive, high-quality datasets. As illustrated in Fig. 1, in collaboration with EPRI, we have built a robust energy-specific corpus, including standards, patents, technical reports, operational datasets, market intelligence, and regulatory documents. This dataset, comprising over 10,000 documents, is processed through Articul8’s advanced data perception module, extracting approximately 400,000 images and 230,000 tables. Automated pipelines ensure this data is accurate, consistent, and relevant, creating a knowledge base that faithfully represents the energy sector’s complexity. The result is a DSM capable of delivering actionable, expert-level insights with unparalleled precision. The extracted entities along with their automated descriptions are stored in a Knowledge Graph. The entities are summarized into auto generated topics, and the topics are further clustered into higher-level cluster of topics to provide an overall summary of the underlying dataset. The resulting knowledge graph, when visualized, depicts the “shape of the data” – with distances showing how close or far away the underlying topics and content are from each other. 

Figure 1: A8 Data Intelligence Platform: from ingestion to true data insights. The top panel illustrates the data ingestion performed by the A8 Platform. The bottom panel shows high-level statistics, and the knowledge graph of autonomously generated topics associated with the dataset used in this experiment. 

The role of reasoning

For domain-specific LLMs and VLMs to serve seasoned energy professionals, advanced reasoning capabilities are critical. These models must not only process vast datasets but also synthesize information to deliver precise, contextually relevant insights. Our approach at Articul8 ensures that energy-focused DSMs achieve this expert-level reasoning through a sophisticated training pipeline and continuous refinement.

A Robust Framework for Expert Reasoning

Our process, depicted in Fig. 2, begins with proprietary Articul8 methods to prepare high-quality, energy-specific data for DSM training, based on the core data provided by EPRI. This data – spanning operational logs, technical reports, and regulatory texts is curated and structured to support multiple training phases. We employ a multi-stage training methodology, including continued pretraining, supervised fine-tuning, reinforcement learning, and custom reasoning optimization. Each stage integrates proprietary algorithms and human-in-the-loop feedback from energy experts, steering the model toward reasoning that mirrors subject matter expertise.

This collaborative refinement allows energy companies to leverage Articul8’s platform alongside their own talent pool, tailoring DSMs to their unique operational and strategic needs. By incorporating company-specific feedback, they enhance the model’s ability to address niche challenges, from grid optimization to regulatory compliance, unique for their company’s applications, keeping their IP safe inside their security perimeter.

Ensuring Reliability and Real-World Impact

To guarantee robustness, we validate our DSMs using rigorous metrics designed for generalizability and effectiveness in real-world energy scenarios. Techniques like adaptive fine-tuning and reasoning customization ensure our models remain accurate and adaptable, even as market dynamics or regulations evolve. The result is a DSM that not only understands the energy sector’s complexities but also reasons through them to provide actionable, expert-level recommendations – empowering companies to optimize performance and stay competitive.

Figure 2: Detailed view of the A8 process for reasoning-centered training of domain-specific models. The top panel illustrates how the data coming from the A8 Platform is used to generate different datasets for model training. The bottom panel illustrates our comprehensive approach to DSM model training.

 

 

To facilitate the data ingestion and training pipeline, we leverage an NVIDIA A100-SXM4-80GB GPU cluster along with NVIDIA-SMI 550.127.05 driver and CUDA version 12.4. We completed the data perception step in less than 12 hours and built the first versions of the multi-stage LLM/VLM DSMs in less than two weeks. 

 

Performance Results: A8-Energy DSMs Outshine General Models

Do you have to be an expert to recognize one?

Not always. Recognizing expert-level reasoning doesn’t always require deep domain knowledge. Our studies demonstrate that A8-Energy DSMs consistently outperform general-purpose models when tasks demand specialized expertise.  Tables 2 and 3 are clear examples where, in comparison with a state-of-the art (SOTA) model, our A8-Energy DSM clearly provides a superior answer. In fact, to our surprise, in Tab. 2, on occasion, Llama 3.3 70B was able to recognize lack of internal knowledge to answer the question.

Table 2: A8 Energy DSM vs Llama 3.3 70B: question requiring detailed knowledge of a topic. Llama 3.3 70B acknowledges that it does not know enough to answer the question.

Table 3: A8 Energy DSM vs Llama 3.3 70B: question requiring detailed knowledge of a topic. Llama 3.3 70B tries to answer the question.

 

 

Superior Visual Understanding in Energy DSMs

When tasks involve visual interpretation, the advantages of domain-specific models (DSMs) over general-purpose models become starkly evident. Tab. 4 illustrates this with a request to describe a specialized energy experimental facility. A8-Energy DSM delivers a precise, detailed narrative, accurately identifying the facility’s purpose and technical context. In contrast, Claude’s response is superficial, wrongly identifying the image as a bowling alley – a critical error that highlights its lack of any energy-specific knowledge.

 

This disparity underscores the DSM’s ability to integrate visual and contextual understanding, honed through training on energy-focused datasets like technical diagrams and operational imagery. For energy professionals, this ensures reliable insights from visual data, enhancing decision-making in complex scenarios.

Table 4: A8 Energy DSM vs Claude 3.5 Sonnet 2 in visual understanding: simple description of an experimental facility.

 

 

The performance of general-purpose models can deteriorate even further if the question requires subject matter expertise. Tab. 5 illustrates a case in which A8-Energy DSM and Claude are put to the test. Similar to before, the DSM is accurate and detailed; while Claude is unable to even identify basic details.

Table 5: A8 Energy DSM vs Claude 3.5 Sonnet 2 in visual understanding: question requiring expert knowledge. Again. 

 

 

Nuanced Performance: DSMs vs. General-Purpose Models

While A8-Energy DSMs consistently outperform general-purpose models in tasks requiring deep expertise, certain scenarios reveal subtler distinctions. These cases highlight the DSM’s nuanced reasoning advantages, even when outputs appear comparable at a glance. 

 

Complex Analysis with Reasoning Edge

Tab. 6 presents a query on evaluating market impacts and collaboration opportunities in energy and water management within EPRI programs. Both A8-Energy DSM and Llama 3.3 70B address market impacts and opportunities across relevant areas. However, the DSM stands out with its structured reasoning, weaving domain-specific insights into a cohesive narrative that reflects expert-level understanding. Llama’s response, while informative, lacks the same depth of contextual analysis, presenting a more generic overview. This underscores the DSM’s ability to not just answer but reason like an energy professional, delivering actionable insights tailored to the sector.

 

Visual Tasks with Text-Heavy Inputs

Table 7 compares A8-Energy DSM with Claude 3.5 Sonnet 2 in describing a text-heavy generic image. Both models produce well-structured responses, likely aided by the image’s abundant text, which shifts the task toward optical character recognition (OCR) proficiency. Claude benefits significantly from this text, achieving a detailed output. Yet, the DSM’s response remains precise and contextually grounded, reflecting its training on energy-specific visual data. This ensures reliability even in less specialized tasks, where general-purpose models may lean heavily on surface-level cues.

 

These examples illustrate that while general-purpose models can occasionally match DSM outputs in specific contexts, A8-Energy DSMs consistently delivers superior reasoning and contextual fidelity – critical for energy professionals seeking dependable, expert-driven solutions.

Table 6: A8 Energy DSM vs Llama 3.3 70B: both provided equally appealing answers.

 

Table 7: A8 Energy DSM vs Claude 3.5 Sonnet 2 in visual understanding: both provided equally appealing answers.

 

 

Conclusion: Transforming Energy with DSM-Powered GenAI in partnership with EPRI

The energy sector stands at a pivotal moment, navigating complex challenges like grid decentralization, demand forecasting, and regulatory compliance. As we have explored in this blog, Articul8’s domain-specific models (DSMs) redefine what’s possible by delivering expert-level reasoning, precision, and context tailored to the industry’s unique demands. Unlike general-purpose models, which often falter in specialized tasks, our DSMs – trained on curated datasets and refined with human expertise – excel in analyzing technical data, interpreting visuals, and providing actionable insights.

 

Beyond standalone models, Articul8’s GenAI platform amplifies impact by empowering energy professionals to explore connections, uncover reasoning, and automate workflows. This holistic approach enables enterprises to optimize decisions, reduce costs, and sharpen competitiveness in a rapidly evolving landscape. From detailed technical queries to nuanced market analyses, our DSMs consistently outperform, as evidenced by comparisons with models like Llama 3.3 70B and Claude 3.5 Sonnet 2.

 

Looking ahead, collaboration across energy and technology stakeholders will be key to scaling these innovations. By sharing expertise and resources, we can accelerate the deployment of GenAI solutions that address the sector’s toughest challenges. We are particularly thankful to the partnership with EPRI and their continued support for making the DSMs better with expert feedback. We are also immensely thankful to NVIDIA for their partnership and collaboration to improve the efficiency of the models. At Articul8, we are committed to leading this charge, and we invite energy professionals, researchers, and innovators to join us. Together, let’s harness DSM-powered GenAI to shape a smarter, more sustainable energy future.