Postgraduate DiplomaData, AI, Simulation & Analytics Applications open

Diploma in Industrial RAG with Documentation and Logs

Online postgraduate diploma of 800 hours in industrial RAG over documentation and logs, with 8 modules covering retrieval-augmented architecture, technical documentation ingestion, log ingestion and analysis, advanced retrieval, reliable generation and agents, evaluation and security, and industrial deployment, with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Design RAG architectures adapted to technical documentation and logs.

02

Ingest complex documents with tables, figures and validity metadata.

03

Index and retrieve log events using hybrid search.

04

Apply advanced techniques such as reranking, GraphRAG and permission filters.

05

Generate answers with citations and hallucination control.

06

Evaluate RAG systems with automatic metrics and expert review.

07

Deploy secure RAG assistants integrated with industrial systems.

Profile

Who it's for

  • Generative AI engineers.
  • Applied machine learning engineers.
  • AI solutions architects.
  • Technical knowledge management specialists.
  • Digital reliability and maintenance engineers.
  • Conversational assistant developers.
  • Engineering or science graduates aiming to enter industry and manufacturing.
Standards & tools

What you'll work with

  • Regulation (EU) 2024/1689 (Artificial Intelligence Act)
  • ISO/IEC 42001:2023 (AI management systems)
  • ISO/IEC 23894:2023 (AI risk management)
  • Regulation (EU) 2016/679 – GDPR (General Data Protection Regulation)
  • ISO/IEC 27001:2022 (information security)
  • NIST AI 600-1 (generative AI profile)
  • ISO/IEC 22989:2022 (AI concepts and terminology)
  • LlamaIndex
  • LangChain
  • Qdrant
  • OpenSearch
  • RAGAS
Syllabus

8 modules

  1. 1.1Language models and knowledge limitations
  2. 1.2RAG architecture: retriever and generator
  3. 1.3Dense embeddings and semantic similarity
  4. 1.4BM25 lexical search
  5. 1.5Hybrid search and rank fusion
  6. 1.6Vector databases: HNSW and IVF indexes
  7. 1.7Context window and token budget
  8. 1.8RAG versus fine-tuning
  9. 1.9Industrial cases: manuals, procedures and logs
  10. 1.10Workshop: first RAG over a maintenance manual
  1. 2.1Extracting PDFs, drawings and datasheets
  2. 2.2OCR and scanned documents
  3. 2.3Tables, diagrams and figures
  4. 2.4Structure-based chunking strategies
  5. 2.5Metadata: equipment, version and validity
  6. 2.6Normalising technical terminology
  7. 2.7Document version control
  8. 2.8Handling multilingual documents
  9. 2.9Tools: Unstructured and Docling
  10. 2.10Practical: ingestion pipeline for plant procedures
  1. 3.1Log formats and parsing with Drain
  2. 3.2Event normalisation and templates
  3. 3.3Time windows and correlation
  4. 3.4Enrichment with asset context
  5. 3.5Indexing in Elasticsearch and OpenSearch
  6. 3.6Embeddings for event sequences
  7. 3.7Incident summarisation from logs
  8. 3.8Volume, retention and sampling
  9. 3.9Sensitive data in logs
  10. 3.10Lab: semantic search over a system's logs
  1. 4.1Reranking with cross-encoders
  2. 4.2Query expansion and rewriting
  3. 4.3HyDE and hypothetical queries
  4. 4.4Multi-hop retrieval across documents
  5. 4.5GraphRAG and knowledge graphs
  6. 4.6Metadata and permission filters
  7. 4.7Retrieval over tables and SQL
  8. 4.8Multimodal RAG with equipment images
  9. 4.9Fine-tuning domain embeddings
  10. 4.10Case study: hybrid search with reranking
  1. 5.1Prompt design with source citations
  2. 5.2Structured responses and JSON
  3. 5.3Reducing hallucinations
  4. 5.4Refusal when evidence is lacking
  5. 5.5Agents with tools and function calling
  6. 5.6Orchestration with LangChain and LlamaIndex
  7. 5.7Model Context Protocol for integrations
  8. 5.8Conversational memory and session context
  9. 5.9Local models versus APIs
  10. 5.10Workshop: diagnostic assistant with citations
  1. 6.1Domain evaluation datasets
  2. 6.2Retrieval metrics: recall@k and MRR
  3. 6.3Faithfulness and relevance with RAGAS
  4. 6.4Human evaluation with experts
  5. 6.5Indirect prompt injection
  6. 6.6Document access control
  7. 6.7Leakage of confidential information
  8. 6.8Risks under the OWASP Top 10 for LLMs
  9. 6.9Logging and auditing of responses
  10. 6.10Practical: RAGAS test bench
  1. 7.1On-premise and cloud architectures
  2. 7.2Latency, cost and semantic caching
  3. 7.3Incremental index updates
  4. 7.4Integration with CMMS and ticketing systems
  5. 7.5Interfaces for field technicians
  6. 7.6Observability with LangSmith and Langfuse
  7. 7.7User feedback
  8. 7.8Governance under the AI Act
  9. 7.9Change management in operations
  10. 7.10Case study: deployment in a maintenance centre
  1. 8.1Selecting the corpus and use case
  2. 8.2Ingesting documentation and logs
  3. 8.3Index and retrieval design
  4. 8.4Generation with citations
  5. 8.5Quantitative and expert evaluation
  6. 8.6Security and access control
  7. 8.7Deployment plan
  8. 8.8Cost and latency analysis
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so it can be combined with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is LlamaIndex, LangChain, Qdrant, OpenSearch and RAGAS, in free, academic or evaluation versions. Real procedures are studied: a query assistant over technical manuals and procedures, semantic search and incident summarisation in logs, and evaluation of answer faithfulness and security. Assessment is continuous by module plus the final integrative project.

Standards covered: Regulation (EU) 2024/1689 (AI Act), ISO/IEC 42001:2023, ISO/IEC 23894:2023, Regulation (EU) 2016/679 (GDPR), ISO/IEC 27001:2022, NIST AI 600-1, ISO/IEC 22989:2022.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the AI Act, ISO/IEC 42001 and ISO/IEC 23894 applied to real cases, not as a theoretical annex.
Industry software:LlamaIndex, LangChain, Qdrant, OpenSearch and RAGAS, in free, academic or evaluation versions.
Final project:a documented industrial RAG assistant over real documentation and logs, with cited generation, quantitative and expert evaluation, access control and a deployment plan, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Corpus and ingestion
Select the corpus and use case and ingest the documentation and logs.
Index and retrieval
Design the index and the retrieval strategy.
Cited generation
Implement answer generation with source citations.
Evaluation and security
Evaluate quantitatively and with experts, and set up security and access control.
Final project
Present the deployment plan, cost and latency analysis and report for review by your tutor.
Career opportunities

Where this takes you

Generative AI engineerApplied machine learning engineerAI solutions architectTechnical knowledge management specialistDigital reliability and maintenance engineerConversational assistant developer
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • An internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

FAQ

Frequently asked questions

The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with LlamaIndex, LangChain, Qdrant, OpenSearch and RAGAS, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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