Mastering Prompt Engineering for GPT Using LLM 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

03/08/26 - 2 days
£2500 +VAT
14/09/26 - 2 days
£2500 +VAT
26/10/26 - 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

Reasoning

  • 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

Conclusions

  • 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.

Generative AI training teaches individuals and teams how to use AI systems that generate text, code, images, and other content — including tools such as ChatGPT, Claude, Gemini, and Microsoft Copilot. JBI's Generative AI training courses are suitable for business professionals, developers, analysts, managers, and technical leaders who want to use AI more effectively in their work, improve productivity, or build AI-powered applications and workflows.
Prompt engineering is the practice of designing and structuring inputs to AI language models to obtain accurate, relevant, and consistent outputs. Effective prompt engineering helps users get better results from AI tools, reduce errors and hallucinations, and build reliable AI-assisted workflows. JBI offers dedicated prompt engineering courses for general LLM use, ChatGPT-specific use, and advanced GPT and LLM applications.
Yes. All JBI Generative AI and LLM training courses are available as live online instructor-led sessions, with the same hands-on exercises and expert instruction as classroom delivery. Online training is available to delegates across the UK and internationally.
A Large Language Model (LLM) is an AI system trained on large amounts of text data to understand and generate human language. LLMs such as GPT-4, Claude, Llama, and Gemini are the foundation of modern generative AI tools including ChatGPT and Microsoft Copilot. JBI's LLM training courses cover how LLMs work, their capabilities and limitations, how to use them effectively through prompt engineering, and how to build applications on top of LLM APIs.
Yes. All JBI Generative AI training courses can be delivered as bespoke closed-group programmes for corporate teams. Content is tailored to your team's role, existing AI experience, specific tools in use, and business objectives. JBI has delivered bespoke Generative AI and LLM training to teams in financial services, professional services, retail, media, the public sector, and technology organisations across the UK.
Retrieval-Augmented Generation (RAG) is a technique that enables AI language models to access and reason over external, up-to-date knowledge sources — such as internal documents, databases, or APIs — rather than relying solely on their training data. RAG is widely used to ground AI responses in factual, organisation-specific information. JBI covers RAG in several courses including Build Agentic AIs with Python, RAG and MCP and Build a Chatbot with Python, RAG and OpenAI.
Model Control Protocol (MCP) is an open standard for connecting AI models to tools, data sources, and external services in a structured and interoperable way. It provides a consistent interface for AI agents to access APIs, databases, file systems, and other resources. JBI offers a dedicated MCP training course covering server and client implementation, Claude API integration, and production deployment of MCP-enabled AI systems.
Yes. JBI Training offers a 3-day LangChain for AI Agents training course covering LLM workflow design, chain construction, agent development, memory systems, retrieval integration, and production deployment using the LangChain framework in Python. The course is designed for developers building LLM-powered applications and AI agent systems.
Prompt engineering focuses on crafting effective inputs to AI models to improve the quality and consistency of outputs — a skill relevant to any user of AI tools, technical or non-technical. Building AI applications with LLMs involves programming against model APIs, designing application architecture, managing context and memory, handling tool use and retrieval, and deploying AI-powered systems. JBI offers training for both — from introductory prompt engineering to advanced LLM application development.
Yes. JBI's Generative AI and LLM training range includes courses for complete beginners such as Harnessing Generative AI, Prompt Engineering for ChatGPT, and AI Prompt Engineering, which require no prior programming or AI experience. Developer-focused courses such as LangChain for AI Agents and Mastering LLMs require programming experience and prior familiarity with AI concepts. Each course page specifies the recommended experience level and prerequisites.
JBI Training regularly reviews and updates its Generative AI and LLM training content to keep pace with the rapid developments in this field. New model releases, updated prompt engineering best practices, emerging frameworks such as LangChain and MCP, and evolving governance requirements all feed into JBI's course refresh cycle. Whether you are learning about ChatGPT, Claude, Gemini, or open-source LLMs, JBI's training reflects how these tools are being used in practice today — not how they worked a year ago.

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