CertifAI Labs: Industrial AI RAG Internship Program
Building AI Resilience for Industry 4.0
This is not a traditional academic course. This is a hands-on, project-based internship designed to transform engineers into AI Architects. Participants will work through the lifecycle of an industrial-grade RAG system, applying it to real-world datasets like technical manuals, switchgear specifications, and safety protocols.
Program Structure & Modules
Week 1: Foundations & The Data Fabric
Goal: Master the art of "Ingestion" for complex industrial data.
Module 1: Advanced Data Ingestion & Refinement
Processing unstructured PDF manuals, CAD metadata, and technical tables.
Implementing Vision-RAG for analyzing industrial diagrams and P&IDs.
Module 2: The Vector Ecosystem
Selecting embedding models for technical domain-specific language.
Hands-on with Vector Databases (Milvus/Chroma) and metadata filtering.
Week 2: Precision Engineering for Retrieval
Goal: Move beyond simple search to "High-Fidelity" context retrieval.
Module 3: Hybrid & Semantic Search
Combining keyword matching for part numbers with semantic search for concepts.
Module 4: Re-Ranking & Context Compression
Using Cross-Encoders to filter "noise" from retrieved results.
Implementing Long-Context strategies to handle massive technical documents.
Week 3: Advanced Architectures & Agency
Goal: Building systems that can "think" and "act."
Module 5: Agentic RAG & Multi-Hop Reasoning
Building agents that can query multiple sources to answer complex questions (e.g., "Compare the maintenance schedule of Building A vs Building B").
Module 6: Hierarchical & Knowledge Graph RAG
Mapping relationships between industrial assets using Graph Databases to improve context.
Week 4: The Production Frontier
Goal: Hardening the system for industrial deployment.
Module 7: Evaluation (The RAGAS Framework)
Automated testing for Faithfulness (no hallucinations) and Relevance.
Red-teaming the system for safety and security.
Module 8: MLOps & Industrial Integration
Implementing Semantic Caching for latency reduction.
Monitoring, tracing, and cost-optimization for enterprise scale.
What You Will Gain from this Internship
Design End-to-End Scalable Pipelines: Learn to build RAG systems that don't just work in a notebook, but scale to 17-building projects and thousands of technical documents.
Master Retrieval Quality: Gain the skills to implement Hybrid Search and Re-ranking, ensuring the AI never misses a critical technical detail.
Navigate Real-World Challenges: Tackle the "Three Pillars of Production": Latency, Cost Optimization, and Scale.
Explore Frontier Architectures: Build Agentic and Multi-hop systems that mimic the reasoning of a senior lead engineer.
Validate with Rigor: Learn how to use frameworks like RAGAS to provide a "Certification of Accuracy" for every AI response.
Production-Ready Portfolio: Build a modular system complete with caching, monitoring, and safety guardrails, ready for deployment in an industrial environment.
The Capstone Project
Interns will be tasked with building a "Digital Technical Assistant".
Scenario: Create a system that retrieves information from the Habshan 55-7 / SS-18 Switchgear technical scope. The assistant must provide exact wiring instructions, safety protocols, and cross-reference multiple vendor manuals without hallucinating a single specification.
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