RAG for Industrial Engineering: Lessons from the Energy Sector

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The Industrial Knowledge Management Challenge

Large energy companies operate on a foundation of institutional knowledge that has been accumulated over decades. Engineering standards, project management methodologies, equipment specifications, safety protocols, inspection records, and lessons learned from thousands of projects form a corpus that is both indispensable and, in practice, nearly impossible to navigate efficiently. At a $70Bn energy company, I faced this challenge firsthand: the Industrial Engineering practice and PMO relied on tens of thousands of technical documents scattered across SharePoint libraries, legacy databases, and departmental silos.

Engineers routinely spent hours searching for the right specification or precedent. Project managers duplicated work because they could not locate prior risk assessments. The problem was not a lack of documentation; it was an abundance of it, combined with no intelligent retrieval layer. Traditional keyword search failed because industrial documents use domain-specific nomenclature, abbreviations, and cross-references that demand semantic understanding.

"The greatest challenge in enterprise AI is not building the model. It is connecting the model to the knowledge that makes it useful."

-- Observation from deploying RAG systems in production

This is a challenge the broader industry recognizes. According to RAND Corporation research, approximately 80% of AI projects fail before deployment, with five root causes identified: misalignment with business needs, poor data infrastructure, lack of clear metrics, insufficient change management, and unrealistic expectations. Only 14% of organizations report being fully ready for AI integration. These numbers set the backdrop for any serious RAG deployment in the industrial sector.

Why RAG for Energy

Retrieval-Augmented Generation has emerged as the dominant pattern for grounding large language models in enterprise data. The approach is straightforward in concept: rather than relying solely on the parametric knowledge baked into a pre-trained model, you retrieve relevant documents at inference time and include them in the prompt context. This grounds the model's output in actual source material, substantially reducing hallucinations when retrieval quality is strong and the system enforces citation and faithfulness checks, while enabling the system to answer questions about proprietary, domain-specific content it was never trained on.

The market trajectory reflects this value proposition. The RAG market reached $1.2 billion in 2024 and is projected to grow to $11 billion by 2030, representing a CAGR of 49.1% [1]. According to a K2view enterprise survey, 86% of organizations that deploy LLMs augment them with some form of RAG [9]. The Document Retrieval segment alone holds 33.5% of market share, and NLP-based approaches lead the technology breakdown at 38.2% [1].

For an energy company, the case is particularly compelling. Engineering teams need precise answers drawn from specific standards and specifications, not creative completions. A wrong answer about a pressure vessel tolerance or a piping material specification is not merely inconvenient -- it is a safety risk. RAG provides the traceability that industry demands: every generated answer can be linked back to its source documents, creating an auditable chain from question to response.

Architecture Decisions: What We Built

Our architecture followed the canonical RAG pipeline but with significant adaptations for the industrial domain. The system needed to handle heterogeneous document types -- PDFs with engineering diagrams, Excel-based equipment registers, Word documents with embedded tables, and legacy scanned documents requiring OCR. We chose a cloud-native deployment, consistent with the 75.9% of RAG implementations that favor cloud infrastructure according to a systematic review of the field [10], though we maintained strict data residency controls required by the company's information security policies.

For the model layer, we adopted GPT-based models for generation, aligning with the 63.6% of RAG systems that rely on GPT-family architectures [10]. The retrieval layer used a hybrid approach combining dense vector search with traditional sparse retrieval, a pattern that consistently outperforms either method alone on technical document corpora. For the vector store and retrieval framework, we relied on standard frameworks like FAISS and Elasticsearch, which 80.5% of implementations use as their retrieval backbone [10].

The key architectural insight was that retrieval quality matters far more than generation quality. An excellent retriever paired with a good generator will outperform a mediocre retriever paired with the best generator available. We invested the majority of our engineering effort in the retrieval and chunking layers rather than in prompt engineering or model selection.

Chunking Strategies for Technical Documents

Chunking -- the process of splitting documents into segments for embedding and retrieval -- is perhaps the most underappreciated component of a RAG system. In the general-purpose literature, recursive text splitting at fixed token windows is the default. For industrial engineering documents, this approach is inadequate.

Technical documents have a hierarchical structure that carries semantic meaning. A specification's Section 3.2.1 inherits context from Section 3.2, which inherits from Section 3. A table of material properties is meaningless if split across two chunks. A cross-reference to "see Section 5.4" requires that the system understand document structure, not just text proximity.

We developed a hierarchical chunking strategy with three tiers:

• Section-level chunks: Each document section (identified by heading structure) becomes a primary chunk, preserving the full context of a discrete topic. These range from 200 to 1500 tokens depending on the section.
• Table-aware chunks: Tables are extracted as complete units with their captions and surrounding explanatory text. A table split across chunks is worse than useless -- it is misleading.
• Parent-child linking: Each chunk maintains metadata about its position in the document hierarchy. When a chunk is retrieved, the system can optionally pull in its parent section for additional context, a technique sometimes called "small-to-big" retrieval.

We also implemented overlap with semantic boundaries. Rather than overlapping by a fixed number of tokens, we extended chunk boundaries to the nearest sentence or paragraph break. This eliminated the common problem of chunks beginning or ending mid-sentence, which degrades both embedding quality and generation coherence.

The RAG Market Landscape

Understanding where RAG adoption stands helps contextualize the architectural decisions any team must make. The market data reveals clear patterns in technology choices, deployment models, and vertical adoption that informed our approach.

Healthcare leads vertical adoption at 36.61%, which is unsurprising given the similar dynamics: vast document corpora, domain-specific language, and high stakes for accuracy. Energy and industrial sectors follow a similar trajectory, driven by the same fundamental need to ground AI outputs in verifiable source material. Large enterprises account for 72.2% of the RAG market, reflecting the scale of document repositories and the organizational complexity that makes RAG most valuable.

Evaluation Frameworks: How to Measure RAG Quality

One of the most common failures in RAG projects is deploying without a rigorous evaluation framework. You cannot improve what you do not measure, and RAG systems have multiple failure modes that require distinct metrics. A system can retrieve the right documents but generate a wrong summary. It can generate a plausible answer from irrelevant sources. It can retrieve relevant content but miss the most critical passage.

We adopted an evaluation framework inspired by the RAGAS methodology (Retrieval Augmented Generation Assessment), which decomposes RAG quality into orthogonal dimensions. Each metric isolates a different component of the pipeline, enabling targeted debugging and improvement.

In practice, we found that Faithfulness and Context Precision were the two metrics that mattered most for our industrial use case. Engineers can tolerate a slightly incomplete answer far better than they can tolerate a fabricated one. A response that says "based on the retrieved documents, the specification requires X" and cites its source is useful. A response that confidently states a wrong value is dangerous.

We maintained an evaluation dataset of approximately 200 question-answer pairs, curated with domain experts, spanning the most common query patterns: equipment specification lookups, procedural questions, project precedent searches, and safety compliance checks. This dataset was refreshed quarterly as new document types were ingested and new query patterns emerged.

"Without an evaluation framework, you are not building a system. You are building a demo. The difference is that a demo only needs to work once."

-- Engineering team principle

Lessons Learned

After months of development, iteration, and production operation, several lessons crystallized that I believe are broadly applicable to any team building RAG for technical or industrial domains.

1. Start with retrieval, not generation

The instinct is to focus on the LLM -- which model, which prompt template, which parameters. Resist this. RAG quality is bounded by retrieval quality. We spent the first six weeks exclusively on the ingestion, chunking, and retrieval pipeline, evaluating it with retrieval-only metrics before ever connecting a generator. This saved us from the common trap of debugging generation issues that were actually retrieval failures.

2. Domain experts are not optional

No amount of engineering cleverness substitutes for domain knowledge. Our engineering SMEs identified chunking failures that no automated test would catch: a table of weld procedures split at the wrong row, a material code parsed as a section header, a cross-reference to an appendix that was not indexed. Embed domain experts into the development team, not the review committee.

3. Hybrid retrieval is not a luxury

Dense vector search excels at semantic similarity but struggles with exact matches on codes, part numbers, and identifiers that are critical in industrial contexts. Sparse retrieval (BM25) handles these precisely but misses paraphrased queries. The hybrid approach -- combining both with a learned re-ranker -- consistently outperformed either method in isolation. This is consistent with the broader trend where standard retrieval frameworks like FAISS and Elasticsearch form the backbone for 80.5% of implementations according to a systematic review [10].

4. The RAG system is a data product

Treat it as such. It needs data quality monitoring, freshness guarantees, usage analytics, and feedback loops. When a source document is updated, the corresponding chunks must be re-indexed. When users report a bad answer, the retrieval and generation traces must be reviewable. This operational dimension is where the 80% AI project failure rate identified by RAND often manifests -- not in the initial build, but in the sustained operation.

5. Plan for what comes next

The RAG landscape is evolving rapidly. Graph RAG, which structures retrieved knowledge as a graph rather than flat passages, is emerging as a technique for documents with complex entity relationships -- exactly the kind found in industrial engineering. Agentic orchestration, where an LLM can plan multi-step retrieval and reasoning strategies, addresses queries that require synthesizing information from multiple documents. Multimodal search, enabling retrieval over engineering diagrams and P&IDs alongside text, is moving from research to production. These are not distant possibilities; they are emerging capabilities in 2024-2025 that any serious RAG architecture should be designed to accommodate.

Conclusion

Building RAG for industrial engineering is not fundamentally different from building RAG for other domains, but the consequences of getting it wrong are higher and the document landscape is more complex. The key insight is that RAG is not a model problem -- it is a data engineering, information architecture, and domain expertise problem. The model is the easiest part. The hard parts are understanding the documents, designing the chunking, tuning the retrieval, and building the evaluation framework that proves the system works.

The market data confirms that RAG is moving from experimental to essential. With the vast majority of enterprises already augmenting their LLMs with retrieval [9], the question is no longer whether to build a RAG system but how to build one that meets the precision and traceability requirements of your specific domain. For industrial engineering, that means treating your RAG pipeline with the same rigor you would apply to any other piece of critical engineering infrastructure: with specifications, testing, quality control, and continuous improvement. Substantial hallucination reduction and significant retrieval speedups are achievable when the fundamentals -- retrieval quality, faithfulness checks, and domain-tuned chunking -- are executed well.