Course Introduction
Retrieval-Augmented Project Knowledge Assistants Development Intensive Course provides professionals with the technical knowledge and practical skills required to design, develop, deploy, and maintain intelligent knowledge assistants that deliver reliable, context-aware answers from project documentation and organizational knowledge repositories. The course combines retrieval-augmented generation (RAG), large language models (LLMs), semantic search, information retrieval, knowledge management, and application development to help organizations transform fragmented project information into accessible, actionable intelligence.
Modern projects generate extensive volumes of information across project charters, business cases, technical specifications, contracts, schedules, budgets, risk registers, meeting minutes, change requests, progress reports, lessons learned, and operational records. Locating the right information quickly can be difficult when documents are dispersed across multiple platforms, stored in inconsistent formats, or maintained by different departments. Participants learn how to build knowledge assistants that retrieve relevant evidence from these sources and use it to generate contextual responses, summarize complex records, identify supporting references, and help project teams make informed decisions.
The course explores the complete RAG architecture, from document ingestion and preprocessing to chunking, embedding generation, vector storage, hybrid retrieval, reranking, context assembly, language-model prompting, and answer generation. Participants examine how retrieval quality, document structure, metadata, search configuration, model selection, and context management influence response accuracy. Through practical exercises, they learn to develop knowledge pipelines that can handle PDFs, spreadsheets, Word documents, technical reports, structured databases, and other project information sources while preserving meaningful relationships between documents, sections, versions, and metadata.
Developing a useful project knowledge assistant requires more than connecting a language model to a vector database. The solution must address hallucinations, incomplete evidence, conflicting documents, outdated information, access permissions, confidential records, and the need for traceable answers. Participants learn how to implement source citations, relevance thresholds, metadata filters, permission-aware retrieval, document version controls, abstention strategies, and human review workflows. The course also covers evaluation techniques for measuring retrieval relevance, answer faithfulness, citation accuracy, completeness, latency, and cost, enabling teams to assess whether an assistant is dependable enough for its intended business use.
The programme emphasizes practical architecture and implementation choices for building scalable, maintainable, and secure knowledge assistants. Participants explore vector databases, keyword search, hybrid retrieval, reranking models, graph-enhanced retrieval, query rewriting, conversational memory, agentic retrieval workflows, application programming interfaces, and integration with enterprise collaboration platforms. They also examine observability, monitoring, prompt injection risks, data leakage prevention, model governance, and lifecycle management. These capabilities help organizations move from experimental prototypes to controlled solutions that can support project management offices, engineering teams, commercial managers, compliance functions, procurement specialists, and executive decision-makers.
By completing the Retrieval-Augmented Project Knowledge Assistants Development Intensive Course, participants will be able to design and implement end-to-end RAG applications tailored to project environments and organizational knowledge needs. They will gain practical experience in preparing project documents, selecting retrieval architectures, configuring embedding and search pipelines, connecting language models, grounding responses in evidence, evaluating answer quality, and deploying secure applications. The course equips participants to develop knowledge assistants that reduce information retrieval effort, improve access to institutional knowledge, strengthen decision support, and support continuous learning across the project lifecycle.
Duration
10 days
Who Should Attend
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AI engineers and machine learning practitioners responsible for developing retrieval-augmented generation applications and enterprise knowledge assistants.
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Software developers and application architects building LLM-powered search, question-answering, summarization, and document intelligence solutions.
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Data engineers responsible for document ingestion, data preparation, metadata management, indexing pipelines, and knowledge retrieval infrastructure.
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Knowledge management professionals seeking to make project documentation, lessons learned, standards, procedures, and institutional knowledge easier to access.
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Project management office professionals developing AI-enabled tools for project reporting, governance, decision support, and organizational learning.
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Project managers and programme managers seeking practical methods for retrieving evidence from complex project records and technical documentation.
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Business analysts and systems analysts translating organizational information needs into requirements for intelligent knowledge assistants.
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Enterprise architects evaluating RAG architectures, vector databases, search services, model integration, deployment approaches, and system interoperability.
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Technical writers and documentation specialists working with large collections of specifications, manuals, procedures, reports, and controlled documents.
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DevOps, MLOps, and platform engineering professionals responsible for deploying, monitoring, securing, and maintaining AI-powered applications.
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Data scientists evaluating embedding models, retrieval methods, reranking techniques, language models, and answer-quality measurement.
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Information security, privacy, and governance professionals involved in assessing access control, data protection, AI risks, and responsible deployment.
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Digital transformation leaders exploring AI-enabled knowledge discovery and automation across project-intensive organizations.
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Consultants and solution architects implementing RAG applications for construction, engineering, infrastructure, consulting, financial services, public-sector, and technology environments.
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Innovation managers and technical team leaders seeking to move knowledge assistant prototypes into measurable, maintainable operational services.
Course Objectives
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Explain the principles of retrieval-augmented generation and distinguish RAG-based knowledge assistants from conventional keyword search, standalone language models, and fine-tuned model solutions.
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Design end-to-end RAG architectures that integrate document ingestion, preprocessing, chunking, embeddings, indexing, retrieval, reranking, prompt construction, language-model generation, and source attribution.
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Develop document ingestion pipelines capable of processing heterogeneous project records while preserving metadata, document structure, version information, access permissions, and traceability.
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Select and configure embedding models, vector databases, lexical search systems, and hybrid retrieval strategies according to content characteristics, relevance requirements, infrastructure constraints, and expected workloads.
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Implement advanced retrieval techniques, including query expansion, query rewriting, metadata filtering, contextual retrieval, reranking, parent-child retrieval, and iterative search for complex project questions.
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Build grounded question-answering workflows that generate evidence-supported responses, provide meaningful citations, communicate uncertainty, and abstain when available sources are insufficient.
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Integrate RAG pipelines with language models, application programming interfaces, web applications, enterprise repositories, and project management information systems.
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Evaluate retrieval and generation quality using representative test datasets, relevance metrics, faithfulness checks, citation verification, human review, and scenario-based performance testing.
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Apply security and governance controls covering permission-aware retrieval, sensitive-data handling, prompt injection defenses, document trust boundaries, audit logging, and responsible model usage.
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Optimize assistant performance by balancing answer quality, retrieval latency, model inference costs, context-window utilization, throughput, scalability, and infrastructure requirements.
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Develop monitoring and maintenance processes for document freshness, index updates, model changes, retrieval drift, failed queries, user feedback, application reliability, and regression testing.
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Produce a practical implementation blueprint for deploying a project knowledge assistant, including architecture decisions, evaluation criteria, security controls, operational responsibilities, and a roadmap for continuous improvement.
Comprehensive Course Outline
Module 1: Foundations of Retrieval-Augmented Generation and Knowledge Assistants
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Examine the principles of retrieval-augmented generation, including how external knowledge retrieval grounds language-model responses in relevant organizational information.
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Compare RAG architectures with traditional information retrieval, standalone LLM prompting, model fine-tuning, long-context approaches, and conventional enterprise search.
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Identify project knowledge use cases such as contract question answering, technical specification lookup, lessons-learned discovery, risk analysis support, meeting retrieval, and document summarization.
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Define assistant requirements through user journeys, information needs, source requirements, response formats, operational constraints, success criteria, and appropriate human oversight.
Module 2: Project Knowledge Sources and Document Ingestion
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Inventory and classify project information sources, including PDFs, Word files, spreadsheets, presentations, scanned records, email exports, databases, and collaboration-platform content.
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Design ingestion pipelines that extract text and structured content while preserving headings, tables, page references, document identifiers, timestamps, ownership, and source metadata.
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Apply optical character recognition and layout-aware extraction methods where required, and assess extraction quality for complex tables, drawings, forms, and scanned technical documents.
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Establish document validation, deduplication, normalization, error handling, provenance tracking, and incremental update procedures for reliable knowledge ingestion.
Module 3: Text Preprocessing, Chunking, and Metadata Engineering
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Compare fixed-size, sentence-based, semantic, structure-aware, and hierarchical chunking strategies for different document types and project knowledge tasks.
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Configure chunk sizes, overlap, boundary rules, and parent-child relationships to preserve context while supporting accurate retrieval and efficient context assembly.
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Develop metadata schemas covering project identifiers, document types, work packages, owners, dates, versions, confidentiality levels, approval status, and lifecycle stages.
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Evaluate preprocessing decisions through retrieval experiments that examine information loss, duplicated context, chunk coherence, token consumption, and source traceability.
Module 4: Embedding Models and Vector Database Architecture
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Explain how embedding models represent text as vectors and examine the influence of semantic similarity, domain vocabulary, multilingual content, and model dimensions on retrieval performance.
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Select vector database technologies and indexing strategies according to data volume, filtering requirements, update frequency, deployment constraints, availability, and operational complexity.
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Implement document embedding pipelines, vector insertion, similarity search, metadata filtering, index maintenance, and retrieval interfaces using suitable development frameworks.
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Compare distance metrics, approximate nearest-neighbor methods, embedding normalization, index configuration, and model alternatives through controlled experiments using representative project questions.
Module 5: Semantic Search, Keyword Retrieval, and Hybrid Architectures
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Implement semantic search for natural-language questions that require conceptual matching across project documents, technical reports, decisions, and lessons learned.
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Configure lexical retrieval methods such as BM25 for exact identifiers, contract clauses, equipment codes, project names, technical terms, and other vocabulary-sensitive searches.
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Combine semantic and lexical retrieval through hybrid search, rank fusion, weighted scoring, and metadata constraints to improve recall and relevance across diverse project content.
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Diagnose retrieval failures involving ambiguous questions, rare terminology, acronyms, numeric values, outdated documents, duplicated passages, and mismatches between user language and source language.
Module 6: Advanced Retrieval, Reranking, and Query Transformation
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Apply query rewriting, decomposition, expansion, and hypothetical-document techniques to improve retrieval for vague, multi-part, or technically complex project questions.
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Use reranking models to reorder candidate passages according to query relevance and assess the trade-offs among quality, computational cost, and response latency.
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Design parent-child retrieval, contextual compression, multi-query retrieval, and iterative retrieval workflows that preserve necessary context without overwhelming the language model.
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Implement metadata-aware retrieval and source prioritization rules for handling document versions, approval status, project scope, recency, authority, and conflicting evidence.
Module 7: Language Models, Prompt Engineering, and Grounded Answer Generation
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Select appropriate language models based on reasoning capability, context-window size, deployment environment, multilingual performance, data-handling requirements, and cost.
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Develop system prompts and answer templates that instruct models to use retrieved evidence, distinguish source facts from inference, preserve important qualifications, and follow required response formats.
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Assemble retrieved context with document titles, page or section references, source identifiers, relevance information, and instructions for handling insufficient or contradictory evidence.
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Implement citation generation, quote verification, structured outputs, answer abstention, clarification requests, and uncertainty communication to reduce unsupported responses.
Module 8: RAG Application Development and Enterprise Integration
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Build a complete RAG application connecting ingestion, indexing, retrieval, reranking, prompt construction, model inference, citation handling, and response presentation.
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Develop application interfaces for conversational queries, document-grounded answers, source inspection, follow-up questions, feedback collection, and retrieval troubleshooting.
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Integrate knowledge assistants with document repositories, collaboration tools, project management platforms, internal APIs, databases, and identity management services where appropriate.
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Implement conversation-state management, session isolation, context-window controls, error handling, retry logic, rate limiting, and API observability for dependable application behavior.
Module 9: Advanced RAG Patterns and Agentic Knowledge Workflows
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Examine graph-enhanced RAG, knowledge graphs, entity relationships, document link structures, and cross-document connections for questions that require more than passage-level similarity.
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Develop agentic retrieval workflows that plan search steps, select tools, refine queries, inspect evidence, and perform bounded follow-up retrieval under explicit control rules.
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Implement multi-stage question answering for questions involving multiple documents, project timelines, contractual dependencies, risk histories, decision records, and evidence synthesis.
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Define tool permissions, maximum iteration limits, execution budgets, stopping conditions, source requirements, and human approval checkpoints for agentic workflows.
Module 10: RAG Evaluation, Testing, and Quality Assurance
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Create representative evaluation datasets containing realistic project questions, expected source passages, acceptable answers, citation requirements, ambiguous cases, and insufficient-evidence scenarios.
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Measure retrieval performance using metrics such as precision at k, recall at k, mean reciprocal rank, and normalized discounted cumulative gain where appropriate.
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Assess generated responses for factual correctness, faithfulness to retrieved evidence, completeness, citation accuracy, relevance, clarity, and adherence to required answer formats.
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Establish automated regression tests, human evaluation rubrics, adversarial test cases, benchmark comparisons, error taxonomies, and release gates for controlled assistant improvement.
Module 11: Security, Privacy, and Responsible Knowledge Access
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Design permission-aware retrieval so users receive only documents and passages they are authorized to access, including when information is embedded, indexed, cached, or summarized.
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Identify prompt injection, malicious document instructions, data exfiltration attempts, sensitive-data exposure, insecure tool use, and other threats affecting RAG applications.
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Apply source trust boundaries, input validation, output filtering, secret management, encryption, access logging, retention controls, and security testing appropriate to the deployment environment.
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Establish governance practices for data ownership, model usage, auditability, human oversight, incident response, privacy obligations, and risk-based approval before operational release.
Module 12: Performance Optimization, Cost Management, and Scalability
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Profile end-to-end latency across document retrieval, reranking, context assembly, language-model inference, network calls, and response rendering to identify performance bottlenecks.
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Optimize retrieval depth, chunk size, embedding dimensions, context length, caching, batching, concurrency, and model selection while monitoring their effect on answer quality.
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Estimate operating costs using document volume, query frequency, token consumption, embedding generation, storage, model pricing, compute requirements, and expected service demand.
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Design scalable deployment patterns with asynchronous ingestion, incremental indexing, workload isolation, queue-based processing, observability, availability controls, and capacity planning.
Module 13: Knowledge Freshness, Version Control, and Conflict Resolution
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Design update pipelines that detect newly created, revised, approved, archived, and deleted project documents and synchronize changes with searchable indexes.
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Implement version-aware retrieval that distinguishes draft, approved, superseded, and archived materials according to explicit organizational rules and source authority.
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Develop conflict-resolution strategies for inconsistent project reports, differing contract versions, changing decisions, conflicting schedules, and incomplete documentation.
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Monitor document freshness, stale-index incidents, broken source references, duplicate records, ingestion failures, and synchronization delays through measurable service indicators.
Module 14: Observability, User Feedback, and Continuous Improvement
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Implement application logging and monitoring for retrieval queries, retrieved source identifiers, latency, model usage, errors, citations, feedback, and system-level performance.
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Build feedback workflows that allow users to report incorrect answers, missing sources, irrelevant passages, outdated content, and usability problems without exposing unnecessary sensitive information.
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Classify failure patterns into ingestion, metadata, retrieval, reranking, prompting, model generation, access control, and user-experience issues to guide targeted remediation.
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Establish continuous improvement cycles that combine evaluation results, usage analytics, controlled experiments, regression testing, model updates, and governance approvals.
Module 15: Deployment, Operations, and Lifecycle Management
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Compare local development, managed cloud services, private-cloud deployments, and self-hosted architectures according to security requirements, scalability, cost, maintainability, and operational capability.
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Package RAG services with appropriate configuration management, secrets handling, environment separation, dependency control, automated testing, deployment pipelines, and rollback procedures.
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Define operational responsibilities for model updates, embedding migrations, index rebuilds, incident response, service monitoring, access reviews, backup, and recovery.
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Prepare deployment documentation covering system architecture, data flows, security boundaries, service dependencies, performance expectations, evaluation results, maintenance procedures, and support escalation.
Module 16: Capstone Project and Production Readiness Assessment
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Design an end-to-end project knowledge assistant for a realistic use case such as contract retrieval, technical document support, lessons-learned discovery, project governance, or implementation reporting.
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Build and demonstrate the ingestion, preprocessing, indexing, retrieval, grounded generation, citation, and user interaction components using an appropriately scoped document collection.
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Evaluate the solution against predefined quality, security, performance, usability, cost, and traceability criteria, documenting limitations and unresolved risks.
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Present a production-readiness roadmap covering architecture decisions, governance approvals, deployment prerequisites, monitoring, user training, maintenance ownership, and measurable benefits.
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.
