Course Introduction
The RAG Data Engineering and Knowledge Retrieval Training Course provides a practical and comprehensive foundation for designing data engineering pipelines that prepare enterprise information for retrieval-augmented generation applications. Participants learn how to collect, transform, enrich, index, retrieve, and govern structured and unstructured data so that AI systems can access relevant organizational knowledge and deliver more grounded, contextual, and reliable responses.
Modern RAG applications depend heavily on the quality and architecture of the underlying data engineering processes. Documents, databases, reports, websites, knowledge repositories, APIs, and operational systems often contain valuable information in inconsistent formats and locations. This course explains how data engineers can create reliable pipelines that transform fragmented information into retrieval-ready datasets while preserving metadata, provenance, permissions, relationships, timestamps, and other contextual information required for trustworthy AI retrieval.
Participants explore the complete RAG data lifecycle, including source discovery, ingestion, extraction, cleansing, normalization, document parsing, chunking, metadata enrichment, embedding generation, vector indexing, hybrid search, and retrieval optimization. The program also examines how structured and unstructured data can be integrated through modern data platforms, vector databases, search engines, knowledge graphs, semantic layers, and APIs. Emphasis is placed on scalable engineering practices that support maintainable and high-performing retrieval architectures.
A major focus of the course is knowledge retrieval quality. Participants learn how to design retrieval pipelines that return relevant, current, diverse, and appropriately authorized information for downstream AI models. They examine semantic search, keyword retrieval, metadata filtering, reranking, query transformation, hybrid retrieval, and contextual retrieval strategies. Practical attention is also given to retrieval evaluation, indexing performance, data freshness, duplicate detection, embedding quality, pipeline monitoring, and the relationship between data engineering decisions and AI response quality.
The course also addresses governance, security, observability, and responsible data engineering for RAG environments. Participants investigate challenges involving sensitive information, access-aware retrieval, data poisoning, stale knowledge, broken pipelines, embedding drift, inconsistent metadata, prompt injection pathways, and unauthorized information exposure. Emerging topics including multimodal RAG, GraphRAG, real-time retrieval, agentic data pipelines, adaptive indexing, streaming embeddings, and AI-native data infrastructure are explored to help professionals prepare for rapidly evolving enterprise AI architectures.
By completing the RAG Data Engineering and Knowledge Retrieval Training Course, participants will be prepared to engineer robust data foundations for retrieval-augmented generation systems. They will understand how to build scalable ingestion and transformation pipelines, create effective retrieval indexes, integrate multiple knowledge sources, evaluate retrieval quality, and implement governance and monitoring controls. The course enables data professionals to move beyond basic data preparation toward future-ready engineering practices that support trustworthy, efficient, and continuously improving AI knowledge retrieval.
Duration
5 days
Who Should Attend
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Data engineers responsible for building pipelines that prepare enterprise information for retrieval-augmented generation and generative AI applications.
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AI engineers developing RAG platforms that depend on reliable ingestion, transformation, indexing, embedding, and knowledge retrieval workflows.
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Data architects designing scalable architectures that connect operational systems, data platforms, documents, vector databases, and enterprise search technologies.
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Machine learning engineers integrating embedding models, retrieval systems, vector stores, reranking technologies, and language models into production AI solutions.
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Data scientists working with semantic search, knowledge discovery, document intelligence, recommendation systems, and retrieval-based machine learning applications.
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Knowledge management professionals seeking to improve organizational information accessibility through structured and semantic retrieval technologies.
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Database and platform engineers supporting vector databases, search infrastructure, metadata management, indexing, and scalable AI data services.
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Research data professionals managing large collections of publications, reports, datasets, evidence, and specialized knowledge resources for AI-assisted discovery.
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Enterprise architects evaluating RAG data infrastructure, hybrid retrieval, knowledge graphs, semantic layers, and AI-ready information architectures.
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Data governance and security professionals responsible for protecting enterprise information throughout ingestion, storage, retrieval, and AI processing workflows.
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Analytics professionals seeking to build reliable knowledge retrieval foundations for conversational analytics, intelligent reporting, and AI-powered research.
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Technology consultants and digital transformation leaders planning scalable data engineering strategies for enterprise RAG adoption and AI knowledge applications.
Course Objectives
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Develop a comprehensive understanding of the data engineering principles required to build reliable, scalable, and maintainable foundations for retrieval-augmented generation systems.
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Design end-to-end RAG data pipelines covering source discovery, ingestion, extraction, transformation, cleansing, enrichment, embedding, indexing, retrieval, and monitoring.
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Apply effective document processing, chunking, metadata enrichment, normalization, deduplication, and classification techniques to improve retrieval-ready data quality.
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Integrate structured and unstructured information from databases, warehouses, lakes, documents, APIs, websites, enterprise applications, and knowledge repositories into unified retrieval architectures.
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Design vector database and search infrastructures that support efficient embedding storage, similarity search, metadata filtering, indexing, scaling, and retrieval operations.
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Implement hybrid retrieval approaches combining semantic search, keyword search, structured filtering, reranking, query transformation, and contextual retrieval for improved knowledge discovery.
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Establish retrieval evaluation practices that measure relevance, precision, recall, ranking quality, source coverage, latency, freshness, and downstream AI response effectiveness.
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Implement data governance and security controls covering provenance, access permissions, sensitive information, retention, auditability, tenant isolation, and secure retrieval processes.
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Monitor and optimize RAG data pipelines by identifying ingestion failures, stale information, embedding drift, indexing problems, metadata inconsistencies, and retrieval degradation.
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Prepare modern RAG data engineering strategies that incorporate multimodal retrieval, GraphRAG, real-time pipelines, streaming embeddings, agentic workflows, adaptive indexing, and AI-native infrastructure.
Comprehensive Course Outline
Module 1: Foundations of RAG Data Engineering and Knowledge Retrieval
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Understanding the relationship between data engineering, knowledge retrieval, embeddings, vector databases, language models, and retrieval-augmented generation architectures.
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Examining how high-quality ingestion, transformation, metadata, indexing, and retrieval processes directly influence the accuracy and usefulness of AI-generated responses.
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Identifying enterprise RAG use cases involving document intelligence, semantic search, research discovery, conversational assistants, knowledge management, and analytical applications.
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Comparing traditional data pipelines with RAG-oriented pipelines and identifying the additional requirements created by semantic retrieval and generative AI.
Module 2: Enterprise Data Discovery, Ingestion, and Source Integration
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Identifying and assessing databases, documents, websites, APIs, data lakes, knowledge repositories, business applications, and other information sources for RAG integration.
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Designing scalable ingestion pipelines that extract information from heterogeneous sources while preserving source identifiers, timestamps, ownership, permissions, and business context.
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Applying batch, incremental, event-driven, and streaming ingestion approaches according to data freshness, volume, operational requirements, and retrieval use cases.
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Managing source changes, schema evolution, failed ingestion, malformed documents, missing records, duplicate information, and inconsistent data across enterprise environments.
Module 3: Data Preparation, Document Processing, and Chunking
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Applying cleansing, normalization, extraction, parsing, transformation, classification, and enrichment techniques to convert raw information into retrieval-ready datasets.
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Designing document chunking strategies that preserve semantic meaning, contextual relationships, headings, tables, references, and other information needed for effective retrieval.
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Comparing fixed-size, semantic, hierarchical, recursive, contextual, and structure-aware chunking approaches for different enterprise and research content types.
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Managing complex information including tables, spreadsheets, presentations, scanned documents, technical reports, web pages, and mixed-format enterprise knowledge assets.
Module 4: Metadata, Embeddings, and Vector Data Engineering
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Designing metadata models that preserve source provenance, document types, permissions, timestamps, ownership, categories, business context, and retrieval attributes.
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Understanding embedding generation, vector dimensions, model selection, domain adaptation, multilingual representations, and embedding lifecycle management.
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Building pipelines that transform prepared content into embeddings while maintaining identifiers and relationships between vectors, source records, documents, and metadata.
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Managing embedding versioning, model changes, re-embedding processes, vector drift, storage requirements, and reproducibility across evolving RAG environments.
Module 5: Vector Databases and Retrieval Infrastructure
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Designing vector database collections, indexes, namespaces, partitions, metadata structures, and storage architectures for scalable knowledge retrieval applications.
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Applying similarity measures and approximate nearest-neighbor indexing techniques to support efficient semantic search across large collections of enterprise information.
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Integrating vector databases with relational databases, document stores, data warehouses, search engines, knowledge graphs, and enterprise application platforms.
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Optimizing vector infrastructure for retrieval latency, throughput, memory consumption, scalability, availability, cost efficiency, and production workload requirements.
Module 6: Hybrid Search and Advanced Knowledge Retrieval
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Combining vector similarity with keyword search, Boolean logic, metadata filters, structured queries, business rules, and enterprise search capabilities to improve retrieval relevance.
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Applying query rewriting, query expansion, reranking, contextual filtering, retrieval diversification, and multi-stage retrieval techniques for complex knowledge discovery tasks.
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Designing retrieval strategies that handle exact identifiers, specialized terminology, numerical values, domain-specific concepts, and semantic relationships within the same search experience.
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Evaluating retrieval pipelines using precision, recall, ranking metrics, source coverage, relevance judgments, latency measurements, and representative enterprise query sets.
Module 7: RAG Data Pipelines and Production Engineering
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Designing production-ready pipelines that connect ingestion, transformation, embedding generation, vector indexing, retrieval, context preparation, and downstream language model applications.
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Implementing workflow orchestration, scheduling, dependency management, retries, validation, exception handling, logging, and automated quality controls for RAG data processes.
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Establishing data lineage and observability across ingestion and retrieval pipelines to identify failures, performance bottlenecks, data quality issues, and retrieval degradation.
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Applying scalable engineering practices for large document collections, high-volume updates, multi-tenant environments, distributed processing, and continuously evolving enterprise knowledge.
Module 8: Governance, Security, Quality, and Retrieval Reliability
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Establishing governance frameworks for source ownership, metadata standards, data lineage, retention, permissions, retrieval policies, and information lifecycle management.
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Implementing access-aware retrieval that ensures users and AI applications receive only information they are authorized to access across enterprise knowledge environments.
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Identifying and mitigating risks involving data poisoning, malicious content, sensitive information leakage, stale knowledge, broken indexes, inaccurate metadata, and unauthorized retrieval.
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Establishing quality monitoring and validation processes that detect missing data, duplicate content, embedding anomalies, ingestion failures, retrieval drift, and other operational problems.
Module 9: Advanced RAG Data Engineering, GraphRAG, and Multimodal Retrieval
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Integrating knowledge graphs, graph databases, semantic layers, relational systems, and vector databases to support richer multi-hop knowledge retrieval and contextual reasoning.
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Engineering multimodal retrieval pipelines that process text, images, tables, audio, video, diagrams, presentations, and other enterprise information formats.
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Exploring agentic data workflows where AI agents coordinate source discovery, ingestion, transformation, retrieval, validation, and knowledge maintenance activities.
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Evaluating advanced architectures for traceability, retrieval quality, security, cost, latency, resilience, human oversight, and responsible autonomous data processing.
Module 10: Emerging Trends and Future-Ready RAG Data Engineering
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Exploring real-time retrieval architectures using streaming data, event-driven pipelines, continuously updated embeddings, incremental indexing, and low-latency knowledge access.
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Examining emerging developments including adaptive retrieval, contextual embeddings, multimodal RAG, GraphRAG, agentic retrieval, AI-native databases, and autonomous data operations.
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Developing implementation roadmaps for enterprise RAG data platforms covering architecture, source prioritization, data readiness, governance, evaluation, scaling, and operational adoption.
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Preparing for future challenges involving data sovereignty, model dependency, vector security, retrieval explainability, autonomous AI agents, continuous evaluation, and trustworthy knowledge infrastructure.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
