About This Course
Generative AI and Large Language Models are reshaping the way organisations communicate, create, analyse, and make decisions. From automating knowledge work to accelerating product development and enhancing customer experiences, these technologies represent one of the most significant shifts in the history of enterprise computing. Yet despite the rapid proliferation of AI tools, most organisations struggle to move beyond experimentation — lacking the structured knowledge, governance frameworks, and practical skills needed to deploy LLMs safely and at scale.
TrainingComplex's Generative AI and Large Language Models (LLMs) Training is a ten-day, deeply immersive programme designed to bridge that gap. Participants will build a solid conceptual foundation — understanding how transformer architectures work, how LLMs are pre-trained and fine-tuned, and why certain models behave the way they do — before progressing to applied, hands-on work with tools such as OpenAI GPT, Google Gemini, Meta LLaMA, and Hugging Face. The programme places equal emphasis on technical fluency and strategic application, ensuring that both practitioners and leaders leave with actionable capabilities.
Central to the course is an exploration of prompt engineering, retrieval-augmented generation (RAG), model evaluation, and responsible AI governance. Participants will design and test prompts, build lightweight AI-powered workflows, evaluate model outputs critically, and learn how to manage risks ranging from hallucination and bias to data privacy and regulatory compliance. Industry-specific use cases drawn from finance, healthcare, legal services, education, and public administration ground every concept in real organisational challenges.
By the end of this programme, participants will be equipped not only to use Generative AI tools confidently, but to advise their organisations on AI strategy, champion responsible adoption, and lead cross-functional AI initiatives with credibility and impact.
Who Should Attend
- IT managers and digital transformation leads seeking to understand and deploy LLM-based solutions
- Data analysts and business intelligence professionals looking to augment their work with generative AI
- Product managers and innovation leads responsible for AI-enabled product development
- Strategy and operations professionals evaluating AI opportunities across the enterprise
- HR and learning & development professionals exploring AI-driven talent and training tools
- Government and public sector officers working on digital government and AI policy
- Marketing and communications professionals leveraging AI for content and campaign automation
- Legal and compliance officers assessing AI risk, regulation, and governance requirements
- Researchers and academics integrating LLMs into knowledge discovery and scholarly work
- Entrepreneurs and startup founders building AI-first products and services
Learning Objectives
- Explain the architecture and training process of Large Language Models, including transformers, tokenisation, and attention mechanisms
- Distinguish between different families of LLMs and select appropriate models for specific organisational use cases
- Apply advanced prompt engineering techniques including chain-of-thought, few-shot, and role-based prompting
- Design and implement retrieval-augmented generation (RAG) pipelines to ground model outputs in authoritative data sources
- Fine-tune pre-trained models on domain-specific datasets using parameter-efficient methods
- Build and test AI-powered workflows and automation scripts using APIs and low-code platforms
- Evaluate LLM outputs using established benchmarks and qualitative assessment frameworks
- Identify and mitigate risks associated with hallucination, bias, data privacy, and adversarial prompting
- Apply AI governance frameworks and responsible AI principles to guide ethical deployment
- Develop a business case and implementation roadmap for a Generative AI initiative within their organisation
- Integrate LLMs with enterprise systems including CRM, document management, and knowledge bases
- Navigate the evolving global regulatory landscape for AI, including the EU AI Act and emerging African frameworks
- Communicate AI strategy and technical concepts clearly to non-technical stakeholders and executive audiences
Course Modules
Module 1: Foundations of Artificial Intelligence and the Rise of Generative AI
- Historical evolution from rule-based AI to neural networks and modern generative systems
- Key distinctions between discriminative and generative AI models
- Overview of major generative modalities: text, image, audio, video, and code
- The generative AI value chain: data, compute, models, platforms, and applications
- Current state of the LLM landscape: proprietary vs. open-source ecosystems
- Why generative AI matters now: economic impact, enterprise adoption trends, and future trajectory
Module 2: How Large Language Models Work: Architecture and Training
- The transformer architecture explained: encoders, decoders, and attention mechanisms
- Tokenisation, embeddings, and how text is represented numerically
- Pre-training objectives: next-token prediction, masked language modelling, and instruction tuning
- Scale laws: how model size, data volume, and compute interact to determine capability
- Reinforcement learning from human feedback (RLHF) and alignment techniques
- Model families and their characteristics: GPT, Gemini, Claude, LLaMA, Mistral, and Falcon
Module 3: Prompt Engineering: Principles and Advanced Techniques
- Anatomy of an effective prompt: instruction, context, input data, and output format
- Zero-shot, one-shot, and few-shot prompting strategies with practical examples
- Chain-of-thought and tree-of-thought prompting for complex reasoning tasks
- Role-based and persona prompting for tone, style, and domain-specific responses
- Prompt chaining and multi-step workflows to decompose complex tasks
- Common prompting pitfalls and systematic approaches to prompt debugging and iteration
Module 4: Working with LLM APIs and Development Platforms
- Introduction to the OpenAI, Google, Anthropic, and Hugging Face APIs and SDKs
- Authentication, rate limits, cost management, and responsible API usage
- Structuring API calls: system messages, user messages, temperature, and token limits
- Low-code and no-code AI platforms: Microsoft Copilot Studio, Zapier AI, and Relevance AI
- Building a simple LLM-powered automation using a REST API and a business trigger
- Version management and reproducibility: why prompt and model versioning matter
Module 5: Retrieval-Augmented Generation (RAG) and Knowledge Grounding
- Why LLMs hallucinate and the fundamental limitations of parametric knowledge
- RAG architecture: document ingestion, chunking, embedding, vector storage, and retrieval
- Choosing and configuring vector databases: Pinecone, Weaviate, ChromaDB, and FAISS
- Embedding models and semantic search: how similarity scoring works in practice
- Designing effective RAG pipelines for enterprise document repositories and knowledge bases
- Evaluating retrieval quality and tuning chunk size, overlap, and retrieval depth
Module 6: Fine-Tuning and Domain Adaptation of LLMs
- When fine-tuning is necessary vs. when prompt engineering or RAG suffices
- Full fine-tuning vs. parameter-efficient methods: LoRA, QLoRA, and adapter layers
- Preparing high-quality domain-specific training datasets: annotation, cleaning, and formatting
- Fine-tuning workflows using Hugging Face Transformers and cloud-based training platforms
- Evaluating fine-tuned models: perplexity, BLEU, ROUGE, and human preference scoring
- Deployment considerations: serving fine-tuned models via APIs and managing model lifecycle
Module 7: LLM Evaluation, Quality Assurance, and Benchmarking
- Dimensions of LLM quality: accuracy, coherence, faithfulness, fluency, and safety
- Standardised benchmarks: MMLU, HellaSwag, TruthfulQA, BIG-bench, and HELM
- Building custom evaluation frameworks aligned to specific organisational use cases
- Automated evaluation using LLM-as-judge approaches and scoring rubrics
- Human-in-the-loop review processes and structured feedback collection
- Continuous monitoring of deployed models for output drift and quality degradation
Module 8: AI Ethics, Bias, and Responsible Generative AI
- Sources of bias in training data and how biases manifest in LLM outputs
- Hallucination: causes, detection strategies, and mitigation through grounding and guardrails
- Fairness, accountability, and transparency as design principles for AI systems
- Privacy risks in LLM deployment: data leakage, membership inference, and PII exposure
- Content safety: toxicity filters, red-teaming, and adversarial prompt injection
- Building an organisational responsible AI policy and model card documentation practice
Module 9: AI Governance, Regulation, and Compliance
- Overview of the EU AI Act: risk tiers, prohibited uses, and compliance obligations
- US Executive Orders and sector-specific AI guidance from financial, healthcare, and defence regulators
- Emerging AI governance frameworks in Africa: AU framework, national policies in Kenya, Nigeria, and South Africa
- Data sovereignty, cross-border data transfer, and cloud hosting considerations for LLM deployments
- Procurement and vendor due diligence for AI products and third-party LLM services
- Establishing an AI governance committee, review process, and incident response protocol
Module 10: Enterprise Integration: Connecting LLMs to Business Systems
- Integration patterns: API-first, middleware orchestration, and embedded AI within SaaS platforms
- Connecting LLMs to CRM systems for intelligent customer interaction and lead qualification
- Document intelligence: extracting, summarising, and classifying content from enterprise repositories
- AI-assisted business process automation using orchestration frameworks such as LangChain and LlamaIndex
- Identity, access management, and audit logging for enterprise AI deployments
- Testing and staging strategies to validate LLM integrations before production rollout
Module 11: Industry-Specific Applications of Generative AI
- Financial services: automated report generation, fraud narrative analysis, and regulatory summarisation
- Healthcare: clinical note drafting, literature synthesis, and patient communication support
- Legal: contract review, clause extraction, legal research assistance, and document drafting
- Education and learning: personalised tutoring, curriculum design, and assessment generation
- Public sector and development: policy drafting, citizen services chatbots, and data-driven reporting
- Marketing and media: personalised content at scale, SEO optimisation, and brand voice consistency
Module 12: AI Strategy, Business Case Development, and Change Management
- Frameworks for identifying high-value AI use cases: impact-effort mapping and value chain analysis
- Building a compelling business case for Generative AI: cost-benefit analysis and ROI modelling
- AI maturity models and organisational readiness assessment tools
- Change management for AI adoption: stakeholder engagement, training, and resistance management
- Creating an AI Centre of Excellence: roles, responsibilities, and operating model
- Developing a phased AI implementation roadmap with milestones, KPIs, and governance checkpoints
Module 13: Capstone: Designing and Presenting a Generative AI Solution
- Scoping a real organisational problem suitable for a Generative AI intervention
- Selecting the appropriate LLM approach: prompt engineering, RAG, or fine-tuning
- Designing the solution architecture, data flow, and integration touchpoints
- Addressing ethical, governance, and compliance requirements within the solution design
- Preparing a structured implementation roadmap and resource plan
- Presenting the solution to a panel of peers and facilitators with structured critique and feedback
Requirements & Prerequisites
Participants should have a working familiarity with basic computing concepts and be comfortable using cloud-based software tools. A background in data analysis, software development, business strategy, or a related professional field is beneficial, though not strictly required. No prior experience with machine learning or AI programming is necessary, as the course is structured to build technical understanding progressively from foundational principles.
Training Methodology
- Guided conceptual lectures using annotated architecture diagrams to demystify transformer models and LLM internals
- Live prompt engineering labs where participants design, test, and iterate prompts in real time using GPT, Gemini, and open-source models
- Case study analysis drawn from documented enterprise AI deployments in finance, healthcare, legal services, and government
- Small-group challenge sessions where teams solve a business problem using an LLM-based approach and present their solution
- Hands-on API integration exercises connecting LLM outputs to simulated enterprise workflows and data sources
- RAG pipeline construction workshops using vector databases and document retrieval tools
- Model evaluation exercises using established benchmarks and peer-review of generated outputs
- Ethics and governance debate simulations exploring real-world AI incidents and regulatory scenarios
- Capstone project development where each participant designs a Generative AI implementation roadmap for their own organisation
- Reflective journalling prompts at the close of each day to consolidate learning and surface questions for the following session
Organisational Benefits
- Accelerated internal adoption of AI tools, reducing time-to-value for Generative AI investments
- Improved quality and consistency of AI-generated content, analysis, and decision support across teams
- Reduced risk of costly AI failures through structured governance, bias mitigation, and compliance awareness
- Stronger internal AI literacy that reduces dependency on external vendors for every AI decision
- Enhanced competitive positioning through faster prototyping and deployment of AI-powered products and services
- Better-informed procurement and vendor evaluation decisions for AI platforms and solutions
- Clear AI strategy and roadmaps aligned with organisational objectives and risk appetite
- Increased staff confidence and engagement with emerging technology, supporting digital culture change
- Improved cross-functional collaboration on AI projects, with shared vocabulary and frameworks
- Organisational capacity to comply with AI-related regulations and avoid reputational and legal exposure
Where Is Generative AI and Large Language Models (LLMs) Training Offered?
This course runs in-person across: Johannesburg, Riyadh, Naivasha, Kigali, Lagos, Mombasa, Accra, Cape Town, Dar es Salaam, Kampala, Abuja, Nairobi, Arusha, Dubai, Addis Ababa, Pretoria, Kisumu, Nakuru, Zanzibar, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
19 Oct 2026 – 30 Oct 2026 |
KES 160,000 |
| Johannesburg, South Africa |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 7,000 |
| Riyadh, Saudi Arabia |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 9,000 |
| Naivasha, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 240,000 |
| Kigali, Rwanda |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 3,800 |
| Lagos, Nigeria |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 5,000 |
| Mombasa, Kenya |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
KES 240,000 |
| Accra, Ghana |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 7,600 |
| Cape Town, South Africa |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 7,600 |
| Dar es Salaam, Tanzania |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 4,600 |
| Kampala, Uganda |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 3,800 |
| Abuja, Nigeria |
In-Person |
2 Nov 2026 – 13 Nov 2026 |
USD 5,600 |
| Kampala, Uganda |
In-Person |
2 Nov 2026 – 11 Nov 2026 |
USD 3,800 |
| Naivasha, Kenya |
In-Person |
2 Nov 2026 – 11 Nov 2026 |
KES 240,000 |
| Accra, Ghana |
In-Person |
2 Nov 2026 – 11 Nov 2026 |
USD 7,600 |
+231 more dates — view live schedule →
Frequently Asked Questions
Do I need a programming background to attend this course?
No formal programming background is required. The course is designed to be accessible to both technical and non-technical professionals, with hands-on labs structured progressively so that participants without coding experience can still engage meaningfully with tools, APIs, and workflows.
What tools and platforms will we use during the training?
Participants will work with OpenAI GPT, Google Gemini, Hugging Face, LangChain, and vector database platforms such as ChromaDB and Pinecone. Access to these tools is facilitated through the training environment, and no prior accounts or licences are required before arrival.
Will I receive a certificate upon completion?
Yes. Participants who complete the full ten-day programme and submit the capstone project will receive a TrainingComplex Certificate of Completion in Generative AI and Large Language Models, which can be shared on professional profiles and referenced in CVs.
Is this course available in an online format?
Yes. The course is offered in hybrid format, meaning it can be attended in person at any of our scheduled locations or via live virtual sessions that replicate the classroom experience with interactive labs, group discussions, and real-time facilitator support.
How current is the course content given how fast AI is evolving?
The curriculum is reviewed and updated before each cohort to reflect the latest model releases, regulatory developments, and industry best practices. TrainingComplex monitors the AI landscape continuously to ensure the content remains relevant and aligned with real-world enterprise needs.
