Design a retrieval-augmented generation pipeline that grounds generated answers in cited engineering document passages.
Oil, Gas & Energy
Applying Generative AI to Engineering Documents and Operating Procedures in Oil and Gas
Apply generative AI and retrieval-augmented systems to search, summarise and draft engineering documents and operating procedures, with the validation controls safety-critical content demands.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Engineering documents and operating procedures accumulate by the thousand across an operating asset's life, and finding the correct revision or reconciling a procedure against the current piping and instrumentation diagram consumes specialist time that could go to higher-value work. This course examines how generative AI, and specifically retrieval-augmented generation built on the organisation's own document set, can support those tasks without introducing unverified information into safety-critical material. Participants work through embedding and indexing engineering documents, designing retrieval pipelines that ground model answers in cited source passages, and building prompts for document summarisation, procedure drafting assistance and cross-document consistency checks. Sessions address hallucination risk, version control, and the human validation gates a generated procedure change must pass before release, alongside ISO/IEC 42001 obligations for AI management systems. Teaching combines walkthroughs of retrieval architectures, a hands-on exercise indexing sample engineering documents, and review of validation workflows. Participants finish able to scope a generative AI use case for engineering documentation, specify its guardrails, and brief engineering and HSE stakeholders on what the technology can and cannot be trusted to do.
Expected Learning Outcomes
Index engineering documents, datasheets and procedures into a searchable vector store with version-aware metadata.
Draft prompts that summarise technical specifications and operating procedures without altering safety-critical values.
Apply cross-document consistency checks that flag mismatches between procedures and current engineering drawings.
Specify human validation gates that a generated procedure change must pass before it is released for use.
Assess hallucination and data leakage risks when connecting a language model to proprietary engineering content.
Align a generative AI deployment for engineering documentation with the governance requirements of ISO/IEC 42001.
Who Should Attend
Engineering document control specialists managing procedures, drawings and technical specifications.
Process safety and HSE professionals who own operating procedures and management of change documentation.
Digital transformation and IT teams evaluating generative AI tools for engineering document workflows.
Facilities and process engineers who query technical documentation as part of daily troubleshooting.
AI governance and risk specialists responsible for approving generative AI use cases in operational settings.
Training and competence teams adapting operating procedures with generative AI drafting support.
Course Modules
Select any module to see its sessions and points.
01Generative AI Foundations for Engineering Content
2 sessions · 8 points
Session 1How Language Models Process Technical Documents
- Explain how large language models generate text and why they can produce plausible but incorrect technical statements.
- Distinguish retrieval-augmented generation from unconstrained generation when grounding answers in source documents.
- Identify the engineering document types, including datasheets, procedures and drawings, suited to each approach.
- Assess data security requirements for connecting proprietary engineering content to a generative AI system.
Session 2Preparing Documents for Retrieval
- Convert scanned and native-format engineering documents into text suitable for indexing and retrieval.
- Chunk technical documents so retrieved passages preserve the context needed for an accurate answer.
- Attach version, revision and approval-status metadata to indexed content to prevent superseded retrieval.
- Build a small vector index from sample specifications and procedures as a working reference architecture.
02Applying Generative AI to Engineering Documents
2 sessions · 8 points
Session 1Search, Summarisation and Drafting Support
- Design prompts that summarise long technical specifications while preserving critical values and tolerances.
- Use retrieval-augmented answers to help engineers locate the clause or datasheet value relevant to a query.
- Draft first-pass updates to operating procedures for human review after a management of change is approved.
- Compare generated summaries against source text to identify where the model has dropped or altered detail.
Session 2Cross-Document Consistency Checking
- Configure consistency checks that compare operating procedures against current piping and instrumentation diagrams.
- Flag terminology and tag-number mismatches between engineering documents raised during a document audit.
- Prioritise flagged inconsistencies by safety significance before routing them to an engineering reviewer.
- Track resolution of flagged inconsistencies through to document revision and reissue.
03Validation, Risk and Human Oversight
2 sessions · 8 points
Session 1Guarding against Hallucination and Error
- Identify the failure modes that lead a language model to fabricate a value, clause or reference that does not exist.
- Design citation requirements so every generated statement links back to a verifiable source passage.
- Set confidence thresholds below which a generated answer is routed to a human reviewer rather than shown directly.
- Test a retrieval-augmented system against known-answer questions to measure its practical accuracy.
Session 2Human Validation and Release Control
- Define the review and sign-off gate a generated procedure change must pass before release into the document register.
- Assign accountability for reviewing generative AI output separately from accountability for the underlying procedure.
- Integrate generative AI drafting output with the existing document management and revision control system.
- Record which passages of a released document were generated, edited or human-authored for future audit.
04Governance and Scaling across the Organisation
2 sessions · 8 points
Session 1AI Management System Requirements
- Apply ISO/IEC 42001 AI management system requirements to a generative AI use case for engineering documentation.
- Document the intended use, limitations and monitoring plan for a generative AI tool applied to procedures.
- Coordinate with data protection and legal teams on retention and confidentiality of indexed engineering content.
- Define incident reporting for cases where generated content reaches a user without required validation.
Session 2Scaling from Pilot to Operating Practice
- Design a pilot that measures time saved and error rate before extending generative AI to further document sets.
- Build a business case that weighs engineering time saved against validation effort and licensing cost.
- Train engineering and HSE staff on appropriate use, including when to escalate rather than trust generated output.
- Plan periodic reassessment of the deployment as document sets, procedures and model versions change.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
Complete your registration
We will contact you within one business day to confirm.
Ready to start?
Reserve your seat and start building the skill.