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Mastering Prompt Engineering for GPT training course

This comprehensive Mastering Prompt Engineering for GPT training course will provide you with the knowledge and techniques needed to harness the full potential of LLMs like GPT.

JBI training course London UK

"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"

Brian Leek, Data Analyst, May 2022

Public Courses

12/08/24 - 2 days
£2500 +VAT
23/09/24 - 2 days
£2500 +VAT
04/11/24 - 2 days
£2500 +VAT

Customised Courses

* Train a team
* Tailor content
* Flex dates
From £1200 / day
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JBI training course London UK

Mastering Prompt Engineering for GPT Training: 

  • Introducing Prompt Engineering
  • Understand the principles of Large Language Models (LLMs), 
  • Define and design effective prompts using strategies like specificity
  •  Learn more about chaining prompts for more refined outputs.
  • Employ advanced prompt design techniques, including use of delimiters
  • Apply techniques to mitigate common LLM issues
  • Utilize various prompting methods, including zero-shot, one-shot, and few-shot prompting
  • Design and implement complex prompt strategies such as Chain of Thoughts (CoT) and persona-based prompts,
  • Expanding the utility and adaptability of LLMs for diverse use-cases.

Introducing Prompt Engineering

  • What is prompt engineering and why is it important?
  • Capabilities of ChatGPT tiers
  • Key concepts in prompt engineering
  • Core components of a prompt: context, instructions, examples
  • Demonstration and discussion of prompt engineering examples
  • Key differences from search queries
  • Examples of effective prompts

Overview of Prompting Approaches

  • A Taxonomy of Prompting: Reductive, transformative, and generative

Basic Prompt Improvements

  • Importance of writing clear and unambiguous prompts
  • Providing context and delimiters
  • Structuring complex prompts
  • Explaining ambiguous concepts and providing definitions
  • Breaking complex prompts into simple steps or multiple prompts
  • Strategies for prompt improvement: Iterating, refining, and chaining prompts
  • Allowing the LLM to demonstrate reasoning and express uncertainty

Limitations of Language Models

  • Description, examples, and mitigation strategies for common LLM issues
  • Hallucinations and Mitigation Strategies
  • Bias and Mitigation Strategies

Understanding Language Models

  • Introduction to LLMs: Types and characteristics, including transformer-based neural nets and Reinforcement Learning from Human Feedback (RLHF)
  • Attention mechanisms
  • Sequence prediction, prompt length, and the context window
  • Characteristics and limitations of LLMs: always generates output, tendency to please people, hallucination, mathematical limitations, data training limitations, and conversation isolation

Giving Examples

  • When to use zero-shot, one-shot, few-shot prompts

Customising the Output

  • Using templates effectively
  • Using delimiters to distinguish the data from the prompt
  • Asking for Structured Output e.g. JSON, XML, HTML etc

Role-Based Prompting

  • Assigning roles for better responses
  • Use case examples

Multiple Perspectives

  • Simulating different viewpoints
  • Improving decision making by taking into account multiple perspectives
  • Converging on a concensus

Adding Personality

  • Why add personality?
  • Defining roles and personality traits
  • Including anecdotes


  • Improving reasoning capabilities
  • Working with maths

Scenarios and Use Cases

  • Data analysis example prompt
  • Designing prompts for text summarization, question answering, and creative writing
  • Writing an Email
  • Generating business reports

Additional Topics

  • More business use cases
  • Leveraging custom GPTs


  • Where to go from here
  • Further resources
  • Course wrap-up
JBI training course London UK

This course is suitable if you have had some exposure to ChatGPT and would like to deepen your skills.

You can gain benefit from the course whether or not you have programming experience.


5 star

4.8 out of 5 average

"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"

Brian Leek, Data Analyst, May 2022

“JBI  did a great job of customizing their syllabus to suit our business  needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and  the Instructor handled this particularly well - very impressive”

Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022



JBI training course London UK



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Prompt engineering is a critical aspect of harnessing the full potential of ChatGPT tiers, enabling users to interact with language models effectively and efficiently. In our course, "Introducing Prompt Engineering," we delve deep into the nuances of this essential skill set.

We kick off by exploring the foundational elements of prompt engineering and its significance in guiding language models to produce desired outputs. Understanding the core components of a prompt, including context, instructions, and examples, forms the cornerstone of our exploration.

Diving deeper, we distinguish between prompt engineering and traditional search queries, unraveling the key differences and showcasing examples of effective prompts tailored for diverse applications.

Our course offers a comprehensive overview of various prompting approaches, from reductive to transformative and generative, empowering participants to craft prompts that elicit optimal responses from language models.

                                                                           Mastering Prompt Engineering for GPT training course FAQs


What is prompt engineering, and why is it important?

Prompt engineering involves crafting clear and precise instructions for language models to generate desired outputs. It plays a pivotal role in guiding language model interactions and ensuring optimal outcomes for various applications.

What are the key components of a prompt?

A prompt typically consists of context, instructions, and examples. These elements provide crucial guidance to language models, helping them understand the desired task and produce relevant responses.

How does prompt engineering differ from traditional search queries?

Prompt engineering focuses on crafting structured instructions for language models, whereas search queries typically involve keyword-based inquiries. Prompt engineering offers more control over language model outputs and enables tailored interactions.

What are some common issues encountered in prompt engineering?

Ambiguity, hallucinations, and bias are common challenges in prompt engineering. These issues can affect the quality and reliability of language model outputs. However, through effective prompt design and mitigation strategies, these challenges can be addressed.

How can prompt engineering be leveraged for different use cases?

Prompt engineering can be applied across various domains, including text summarization, question answering, creative writing, and business report generation. By customizing prompts to suit specific tasks and objectives, users can optimize language model interactions for diverse applications.

What role does personality play in prompt engineering?

Incorporating personality traits and anecdotes into prompts can enhance the user experience and foster more engaging interactions with language models. By adding personality elements, prompts can be tailored to match user preferences and expectations.

How can prompt engineering improve decision-making processes?

By simulating different viewpoints and leveraging multiple perspectives, prompt engineering can facilitate more informed decision-making. Participants learn to design prompts that encourage critical thinking and consider diverse viewpoints, ultimately leading to better outcomes.

What are some additional topics covered in the course?

The course explores advanced topics such as role-based prompting, reasoning capabilities, leveraging custom GPTs, and exploring business use cases. Participants gain insights into cutting-edge techniques and best practices for maximizing the effectiveness of prompt engineering in AI development.

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