October 2026 · Weekly batch
03 Oct 2026
09:00–15:00 · flexible hours
$799
5 of 25 seats available
Generative AI
Large Language Models (LLMs) Fundamentals in London, United Kingdom
Large Language Models (LLMs) Fundamentals is a comprehensive training course designed to help learners understand the core concepts, architecture, and applications of modern Large Language Models. Available in London, United Kingdom , the course introduces participants to LLMs, Generative AI, Natural Language Processing (NLP), transformers, tokens, embeddings, attention mechanisms, model training, fine-tuning, and inference . Learners explore how LLMs process language, generate responses, and support AI-powered applications such as chatbots, content generation, summarization, question answering, and intelligent automation. The course is suitable for students, developers, IT professionals, data professionals, business users, and AI enthusiasts who want to build foundational LLM expertise. Through practical learning and real-world examples, participants develop an understanding of how LLM technologies can be integrated into modern AI solutions. Enroll in LLM Fundamentals training in London, United Kingdom to strengthen your AI knowledge, understand emerging LLM technologies, and establish a foundation for advanced learning in Generative AI, RAG, AI agents, and LLM application development.
5 daysFoundation4.8 · 3,200 reviews
Training calendar
Upcoming four-month course schedule
A new weekday and weekend batch starts every week. Each batch runs for 5 training days, based on the course duration of 5 days.
October 2026 · Weekly batch
10 Oct 2026
09:00–15:00 · flexible hours
$799
10 of 25 seats available
October 2026 · Weekly batch
17 Oct 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
October 2026 · Weekly batch
24 Oct 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
October 2026 · Weekly batch
31 Oct 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
November 2026 · Weekly batch
07 Nov 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
November 2026 · Weekly batch
14 Nov 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
November 2026 · Weekly batch
21 Nov 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
November 2026 · Weekly batch
28 Nov 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
December 2026 · Weekly batch
05 Dec 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
December 2026 · Weekly batch
12 Dec 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
December 2026 · Weekly batch
19 Dec 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
December 2026 · Weekly batch
26 Dec 2026
09:00–15:00 · flexible hours
$799
25 of 25 seats available
January 2027 · Weekly batch
02 Jan 2027
09:00–15:00 · flexible hours
$799
25 of 25 seats available
January 2027 · Weekly batch
09 Jan 2027
09:00–15:00 · flexible hours
$799
25 of 25 seats available
January 2027 · Weekly batch
16 Jan 2027
09:00–15:00 · flexible hours
$799
25 of 25 seats available
January 2027 · Weekly batch
23 Jan 2027
09:00–15:00 · flexible hours
$799
25 of 25 seats available
January 2027 · Weekly batch
30 Jan 2027
09:00–15:00 · flexible hours
$799
25 of 25 seats available
Course curriculum
What you’ll learn
- Module 1: Introduction to Large Language Models
- Understanding Large Language Models and their role in Generative AI.
- Evolution of Natural Language Processing and language models.
- Exploring capabilities, limitations, and common LLM use cases.
- Understanding the LLM ecosystem and applications in London, United Kingdom.
- Module 2: Natural Language Processing Fundamentals
- Introduction to text processing and language representation.
- Understanding vocabulary, tokens, tokenization, and text encoding.
- Exploring semantic relationships and language representations.
- Understanding how NLP supports modern LLM applications.
- Module 3: Transformer Architecture
- Introduction to the Transformer architecture.
- Understanding attention and self-attention mechanisms.
- Exploring encoder and decoder components.
- Understanding positional encoding and its role in language processing.
- Module 4: Tokens, Embeddings, and Context
- Understanding tokenization and token management.
- Exploring word, sentence, and contextual embeddings.
- Understanding context windows and sequence processing.
- Examining how context influences LLM responses in London, United Kingdom.
- Module 5: LLM Training and Pre-Training
- Understanding datasets and data preparation for LLM training.
- Exploring pre-training and language modeling objectives.
- Understanding computational requirements and training infrastructure.
- Exploring challenges associated with large-scale model training.
- Module 6: Fine-Tuning and Instruction Tuning
- Understanding the purpose of model fine-tuning.
- Exploring supervised fine-tuning and instruction tuning.
- Understanding parameter-efficient fine-tuning concepts.
- Identifying scenarios where customization can support specific AI applications.
- Module 7: LLM Inference and Generation
- Understanding the LLM inference process.
- Exploring text generation and decoding strategies.
- Understanding temperature, top-k, and top-p sampling.
- Examining factors that influence model responses and performance in London, United Kingdom.
- Module 8: LLM Applications and Use Cases
- Exploring conversational AI and virtual assistants.
- Understanding text generation, summarization, and classification.
- Exploring question answering and information extraction.
- Examining LLM applications across business and technology environments.
- Module 9: LLM Evaluation and Responsible AI
- Understanding approaches for evaluating LLM quality and performance.
- Exploring accuracy, relevance, consistency, and response quality.
- Understanding hallucinations, bias, privacy, and security considerations.
- Applying responsible AI principles to LLM applications in London, United Kingdom.
- Module 10: Practical LLM Applications and Future Trends
- Exploring practical workflows using modern LLM technologies.
- Understanding LLM-powered application development at a foundational level.
- Introduction to RAG, AI agents, and LLM application architectures.
- Exploring emerging LLM trends and pathways for advanced AI learning in London, United Kingdom.
Entry requirements and prerequisites
- Basic AI Knowledge – A general understanding of Artificial Intelligence and Machine Learning concepts is helpful for learners in London, United Kingdom.
- Generative AI Familiarity – Basic awareness of Generative AI and common AI applications is recommended.
- Programming Knowledge – Basic Python programming knowledge is beneficial but not mandatory for understanding LLM concepts and practical exercises.
- Basic Mathematics – Familiarity with basic mathematical concepts such as probability, statistics, and linear algebra can help learners understand selected LLM concepts.
- Computer Skills – Basic computer and internet skills are expected for accessing AI tools, course resources, and practical activities in London, United Kingdom.
- No Prior LLM Experience Required – Learners do not need previous professional experience with Large Language Models, transformers, or LLM development.
Career value
Reasons to choose this course
Build Strong LLM Foundations
Understand the core concepts, terminology, and technologies behind Large Language Models (LLMs) through structured training in London, United Kingdom.
Understand Transformer Architecture
Learn the fundamental principles of transformers, attention mechanisms, tokens, and embeddings that power modern LLMs.
Explore LLM Training Processes
Gain insights into pre-training, fine-tuning, instruction tuning, and inference workflows used in modern language models.
Learn Practical LLM Applications
Explore how LLMs support chatbots, content generation, summarization, question answering, and intelligent automation in London, United Kingdom.
Develop AI Technology Awareness
Understand the capabilities, limitations, and practical considerations of modern LLM technologies.
Prepare for Advanced AI Learning
Establish a foundation for progressing into RAG, prompt engineering, AI agents, LLM application development, and enterprise AI.
Advantages
Turn learning into practical capability
Understand LLM Capabilities and Limitations
Learn to recognize where LLMs perform effectively and where human validation, additional data, or specialized techniques may be required.
Interpret Model Outputs More Effectively
Develop knowledge of tokens, context, embeddings, and inference to better understand how LLMs produce responses.
Bridge Theory and AI Applications
Connect fundamental LLM concepts with practical solutions such as conversational assistants, text processing, and knowledge-based applications in London, United Kingdom.
Make Better Technology Decisions
Gain foundational knowledge that can help learners understand important considerations when selecting or working with different LLM technologies.
Strengthen AI Project Collaboration
Develop terminology and conceptual knowledge that supports communication between technical teams, business stakeholders, and AI professionals.
Create a Pathway to LLM Specialization
Build the conceptual base needed to explore advanced areas such as fine-tuning, RAG, LLMOps, evaluation, and production AI applications in London, United Kingdom.
Course includes
Review the instruction, learning resources and learner support provided with this programme. The course-specific details below explain exactly what is included in your training experience.
- Comprehensive LLM Curriculum – Learn LLM fundamentals, NLP, transformers, tokens, embeddings, attention mechanisms, training, fine-tuning, inference, evaluation, and real-world applications in London, United Kingdom.
- Live Instructor-Led Training – Participate in structured sessions with expert guidance, demonstrations, discussions, and practical explanations of Large Language Model technologies.
- NLP and Language Processing Concepts – Understand tokenization, text representation, embeddings, semantic relationships, and foundational Natural Language Processing concepts.
- Transformer Architecture Learning – Explore transformer components, self-attention, positional encoding, encoder-decoder concepts, and their role in modern LLMs.
- LLM Training and Fine-Tuning – Understand pre-training, supervised fine-tuning, instruction tuning, and parameter-efficient customization approaches.
- Inference and Text Generation – Learn how LLMs generate responses and explore decoding parameters such as temperature, top-k, and top-p.
- Hands-On LLM Exercises – Apply foundational concepts through practical activities involving language models, text generation, analysis, and AI-powered workflows.
- LLM Application Use Cases – Explore conversational AI, summarization, question answering, content generation, information extraction, and intelligent automation in London, United Kingdom.
- LLM Evaluation and Responsible AI – Learn about response quality, hallucinations, bias, privacy, security, and responsible deployment considerations.
- Introduction to Advanced LLM Technologies – Gain foundational exposure to RAG, AI agents, LLM application architectures, and emerging AI technologies.
- Learning Resources and Materials – Access supporting resources designed to reinforce LLM concepts and encourage continued AI learning.
- Course Completion Certificate – Receive a StepMerit Course Completion Certificate upon successfully completing the course requirements.
Exam details
Understand the applicable assessment approach, preparation support and exam-readiness guidance before planning your certification attempt.
- Exam Level: Beginner–Intermediate – Large Language Models Fundamentals
- Exam Type: Online computer-based examination consisting of multiple-choice questions and practical LLM-based tasks in London, United Kingdom.
- Number of Questions: 40 questions
- Exam Duration: 90 minutes
- Passing Score: 70% (28 out of 40)
- Question Format: Multiple-choice questions covering LLM fundamentals, NLP, transformers, tokens, embeddings, attention mechanisms, training, fine-tuning, inference, and evaluation.
- Practical Assessment: Candidates may complete practical tasks involving LLM interaction, text generation, model parameters, prompt-based activities, and analysis of LLM outputs.
- Key Exam Topics: LLM architecture, transformer models, tokenization, embeddings, context windows, pre-training, fine-tuning, inference, decoding strategies, LLM applications, evaluation, and responsible AI.
- Prerequisites: Basic knowledge of Artificial Intelligence and Generative AI is recommended. Basic Python knowledge is beneficial but not mandatory.
- Assessment Platform: Online assessment through the StepMerit platform.
- Certification: Successful candidates receive a StepMerit Course Completion Certificate.
- Exam Preparation: Hands-on exercises, demonstrations, and practical LLM activities help learners prepare for the assessment in London, United Kingdom.
Course conclusion
See how the programme brings the learning together through review, practical application and clear next steps for using the skills after training.
The Large Language Models (LLMs) Fundamentals course provides a strong foundation for understanding modern LLM technologies, Generative AI, Natural Language Processing (NLP), transformer architecture, tokens, embeddings, attention mechanisms, model training, fine-tuning, inference, and LLM evaluation. Through practical examples and hands-on learning, participants develop the knowledge required to understand how LLMs generate content and power AI applications such as chatbots, question answering, summarization, content generation, and intelligent automation.
This course is suitable for students, developers, IT professionals, data professionals, business users, and AI enthusiasts seeking practical LLM training in London, United Kingdom. Learners also gain foundational knowledge that can support advanced studies in RAG, AI agents, prompt engineering, LLM application development, and enterprise Generative AI.
Enroll in Large Language Models (LLMs) Fundamentals training i
Who should attend
Target audience for Large Language Models (LLMs) Fundamentals
- Students and Graduates – Students and recent graduates who want to build foundational LLM, Generative AI, and NLP skills for emerging technology careers in London, United Kingdom.
- AI and Machine Learning Professionals – AI and ML professionals seeking to strengthen their understanding of Large Language Models, transformers, embeddings, and modern Generative AI technologies in London, United Kingdom.
- Software Developers – Developers who want to understand LLM concepts and explore the development of AI-powered applications and intelligent solutions in London, United Kingdom.
- IT and Technology Professionals – IT professionals looking to expand their technical knowledge of LLMs, NLP, AI applications, and language-based technologies in London, United Kingdom.
- Data Professionals – Data analysts, data scientists, and related professionals interested in understanding LLM data processing, embeddings, model training, and evaluation in London, United Kingdom.
- Business Analysts – Business analysts who want to identify practical LLM and Generative AI use cases for business processes, automation, and decision support in London, United Kingdom.
- Product and Project Professionals – Product managers, project managers, and technology leaders seeking foundational knowledge to collaborate on LLM and AI application projects in London, United Kingdom.
- Content and Marketing Professionals – Content creators and marketing professionals interested in using LLMs for content generation, summarization, research, and productivity workflows in London, United Kingdom.
- IT Consultants and Solutions Professionals – Consultants and solution professionals who want to understand LLM capabilities and support AI solution planning and implementation in London, United Kingdom.
- Researchers and AI Enthusiasts – Researchers and AI enthusiasts seeking structured LLM training to explore language models, Generative AI, and emerging AI technologies in London, United Kingdom.
- Career Switchers – Professionals transitioning into AI and technology careers who want to establish practical foundational knowledge of LLMs and Generative AI in London, United Kingdom.
- Business Owners and Entrepreneurs – Entrepreneurs and business owners interested in understanding how LLMs can support automation, customer engagement, content creation, and AI-driven business solutions in London, United Kingdom.
Job roles
Explore the professional roles where the knowledge and practical capabilities developed in this course can be applied.
- Generative AI Associate – Apply foundational knowledge of LLMs, Generative AI, prompt-based applications, and AI workflows in entry-level roles in London, United Kingdom.
- AI Business Analyst – Analyze business requirements and identify opportunities to use LLMs, NLP, and Generative AI for process improvement in London, United Kingdom.
- LLM Application Developer – Support the development of AI-powered applications using foundational knowledge of LLMs, APIs, text generation, and model interaction in London, United Kingdom.
- AI Content Specialist – Use LLM technologies for content generation, summarization, ideation, and content workflows across organizations in London, United Kingdom.
- AI Productivity Specialist – Implement LLM-powered tools and workflows to improve professional productivity, automation, and knowledge work in London, United Kingdom.
- AI Operations Associate – Support the deployment, monitoring, documentation, and day-to-day operation of LLM-based AI solutions in London, United Kingdom.
- NLP Associate – Apply foundational concepts of Natural Language Processing, tokenization, embeddings, and language models to AI projects in London, United Kingdom.
- AI Research Assistant – Assist with LLM research, experimentation, model evaluation, and Generative AI projects in London, United Kingdom.
- Junior AI Consultant – Help organizations explore LLM use cases, AI solutions, and Generative AI adoption based on business requirements in London, United Kingdom.
- Generative AI Support Specialist – Provide technical and functional support for LLM-powered applications, AI tools, and Generative AI workflows in London, United Kingdom.
Career and organisation benefits
Understand how this training can support individual career development while helping organisations strengthen capability, consistency and performance.
- Build Strong LLM Knowledge – Develop a clear understanding of Large Language Models, their architecture, capabilities, and applications through structured LLM training in London, United Kingdom.
- Understand Transformer-Based AI – Learn how transformers, attention mechanisms, tokens, and embeddings support modern language models and Generative AI solutions in London, United Kingdom.
- Improve AI Application Skills – Gain foundational knowledge for working with LLM-powered applications such as chatbots, content generation, summarization, and question answering in London, United Kingdom.
- Strengthen Generative AI Expertise – Build practical awareness of how LLMs contribute to modern Generative AI technologies and intelligent automation in London, United Kingdom.
- Develop LLM Evaluation Awareness – Understand key concepts for evaluating LLM outputs, model performance, reliability, and responsible AI considerations in London, United Kingdom.
- Support AI Project Collaboration – Communicate more effectively with AI engineers, developers, data professionals, and business teams working on LLM projects in London, United Kingdom.
- Prepare for Advanced AI Courses – Establish a strong foundation for advanced learning in RAG, AI agents, prompt engineering, fine-tuning, and LLM application development in London, United Kingdom.
- Expand Career-Ready AI Skills – Strengthen foundational LLM and Generative AI knowledge that can support emerging AI career opportunities in London, United Kingdom.
Helpful answers for planning your training
Frequently asked questions
LLMs are AI models that understand and generate human-like language for various applications.
You will learn LLMs, NLP, transformers, tokens, embeddings, training, fine-tuning, and inference.
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