Overview
Blended Learning – the best of both ways to learn.
This course blends the flexibility of self-paced learning with the structure of live, instructor-led sessions. You'll learn from world-class industry experts and gain practical skills to drive meaningful results in your workplace. Our digital platform also empowers you to track your progress and manage your learning journey effectively.
This 3 day course provides an in-depth exploration of Large Language Models (LLMs) and their applications and covers key Generative AI skills expected of AI Engineers.
Designed for those with existing knowledge of python, python data packages, and desirably machine learning.
This instructor led course will cover essential topics such as LLM architectures, prompt engineering, model monitoring and observability, and working with both vendor-managed and open source models.
Additionally, the course introduces the use of agentic workflows and prompt evaluation techniques to help organisations effectively manage AI-driven processes.
These later topics are an excellent taster of our Building AI Agents with Python course for those progressing in their AI Engineering journey.
Prerequisites
Participants should have:
- Experience with base Python including collections and experience with common packages for data handling such as NumPy or Pandas.
- If participants do not yet have the expected prior knowledge this can be gained through QADHPYTHON Data Handling with Python
- Desirable experience with Machine Learning development processes
- If participants do not yet have this prior knowledge this can be gained through either QAIDSDP Introduction to Data Science for Data Professionals or QADSMLP Data Science and Machine Learning with Python.
Target audience
This course is designed for:
- Data Scientists
- Software Developers
- Machine Learning Engineers
- AI Engineers
- DevOps Engineers
Learning Objectives
By the end of this course, learners will be able to:
- Identify and apply knowledge of Large Language Models to build Generative AI applications
- Interact with LLMs with advanced prompt engineering techniques
- Select and build with the right models and architectures for an AI application, including when RAG and fine-tuning are appropriate
- Evaluate, monitor, and optimize speed and performance in an AI system in deployment
Course Outline
01 Overview of Large Language Models
- Discuss the evolution and architecture of LLMs
- Differentiate between LLM architectures and their applications
- Apply basic prompt engineering techniques to interact with LLMs
02 Transformer Model Architecture
- Describe core transformer components including attention mechanisms
- Discuss model structure and recent architecture improvements
- Identify techniques to speed up generation, such as caching keys and values
- Investigate encoder only, decoder only, and encoder-decoder models – what type of tasks and applications they are used for
03 Tokens and Embeddings
- Discuss what tokens and embeddings are
- Describe different tokenization and embedding approaches
- Discuss the immense usefulness of embeddings
04 Using Pre-Trained Language Models
- Describe what a pre-trained language model is
- Describe different types of pre-trained models
- Consider the specific details for pre-trained models
- Apply a pre-trained model to a text classification task
05 Prompt Engineering
- Discuss why prompt structure and content is important
- Develop effective prompts to improve LLM responses
- Write prompts with different components and discover how they affect LLM responses
06 Advanced Text Generation Techniques and Tools
- Describe and implement agentic workflows for multi-step reasoning and task automation
- Discuss memory management and conversation handling in LLM applications
- Implement the architecture and components of RAG pipelines, including vector stores and retrievers
- Compare the pros and cons of agentic techniques in real-world scenarios
07 Training and Finetuning Language Models
- Describe how embedding models are trained and discuss techniques like contrastive learning
- Apply techniques to continue pre-training language models, such as masked language modelling
- Apply techniques for fine-tuning classification models, such as supervised fine-tuning and preference tuning
08 Evaluating, Deploying, and Observing Models
- Identify the metrics that can be used to evaluate LLMs and the difficulties in evaluating LLMs
- Outline best practices and typical operations when deploying models
- Discuss the importance of model monitoring and detecting drift – what to do when problems are identified
09 Techniques for Latency Reduction and Model Optimization
- Describe the trade-offs between model performance, size, and inference speed
- Identify the role of techniques such as quantization
- Apply quantization to optimize model performance
Exams and assessments
Learning outcomes are assessed through activities within this Instructor-Led course.
Delivery Method
This Blended Learning course consists of two key stages.
Self-Paced Learning
- Up to 1 hour, completed over a 4-week period prior to the live event.
- It is recommended that the self-paced learning is completed prior to joining the live event.
- It is recommended that learners have a minimum of 4 weeks between the course booking and the instructor-led live event to complete the necessary hours of learning.
- The self-paced learning is available 4 weeks prior to the live event and for 12 months following the live event.
Instructor-Led Live Event
- This course has a 3-day live event.
Frequently asked questions
How can I create an account on myQA.com?
There are a number of ways to create an account. If you are a self-funder, simply select the "Create account" option on the login page.
If you have been booked onto a course by your company, you will receive a confirmation email. From this email, select "Sign into myQA" and you will be taken to the "Create account" page. Complete all of the details and select "Create account".
If you have the booking number you can also go here and select the "I have a booking number" option. Enter the booking reference and your surname. If the details match, you will be taken to the "Create account" page from where you can enter your details and confirm your account.
Find more answers to frequently asked questions in our FAQs: Bookings & Cancellations page.
How do QA’s virtual classroom courses work?
Our virtual classroom courses allow you to access award-winning classroom training, without leaving your home or office. Our learning professionals are specially trained on how to interact with remote attendees and our remote labs ensure all participants can take part in hands-on exercises wherever they are.
We use the WebEx video conferencing platform by Cisco. Before you book, check that you meet the WebEx system requirements and run a test meeting to ensure the software is compatible with your firewall settings. If it doesn’t work, try adjusting your settings or contact your IT department about permitting the website.
How do QA’s online courses work?
QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course.
Once you have purchased the Online course and have completed your registration, you will receive the necessary details to enable you to immediately access it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.
All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.
When will I receive my joining instructions?
Joining instructions for QA courses are sent two weeks prior to the course start date, or immediately if the booking is confirmed within this timeframe. For course bookings made via QA but delivered by a third-party supplier, joining instructions are sent to attendees prior to the training course, but timescales vary depending on each supplier’s terms. Read more FAQs.
When will I receive my certificate?
Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.