About this LLM Prompt Engineering course
Harnessing the full capabilities of Large Language Models (LLMs) depends entirely on your ability to communicate with them deterministically. Prompt Engineering is the technical discipline of designing and refining inputs to guide models toward accurate, context-aware, and reproducible outputs. This 2-day intensive workshop provides software engineers and technical professionals with the structural framework required to interact with LLMs effectively, whether through web-based interfaces or programmatically via APIs.
We treat prompting as an engineering workflow rather than a trial-and-error exercise. You will begin by exploring the underlying mechanics of modern transformers, analyzing key execution parameters like token allocation and temperature settings. We address core system limitations - such as hallucination vectors and structural bias, before moving rapidly into advanced logic patterns, including few-shot learning, Chain-of-Thought reasoning, and system-role delineation.
Through hands-on technical labs, the workshop transitions into API orchestration, covering authentication setup, response parsing, and state management. You will also explore structural patterns like Retrieval-Augmented Generation (RAG) and Function Calling, which allow LLMs to interact safely with external databases and applications. By the end of the course, you will leave with a reusable library of prompting patterns and the diagnostic skills to build secure, responsible AI integrations.
Instructor-led online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the World.
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By the end of this course, attendees will be able to:
- Explain what Large Language Models (LLMs) are and why Prompt Engineering is essential for effective interaction.
- Understand key LLM parameters (tokens, temperature, etc.) and how they influence output, as well as common LLM limitations.
- Apply core Prompt Engineering techniques, including zero-shot, few-shot, Chain-of-Thought, and role prompting, to guide LLM responses.
- Structure prompts effectively using roles, delimiters, and clear instructions.
- Perform basic interactions with LLMs programmatically via APIs.
- Understand the concepts of Function Calling/Tool Use and Retrieval-Augmented Generation (RAG) in the context of LLM applications (overview).
- Apply Prompt Engineering techniques to practical personal and code productivity use cases.
- Identify common LLM limitations and ethical considerations related to prompting and output.
- Understand the rapid pace of development and future trends in the AI and LLM landscape.
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This 2-day Prompt Engineering and LLM training course is designed for anyone who wants to leverage the power of Large Language Models effectively, whether through user interfaces or by integrating them into applications and workflows. It is ideal for:
Developers, data scientists, and engineers looking to understand how to interact with LLMs via APIs and incorporate them into their work.
Analysts, researchers, and knowledge workers seeking to improve their productivity using LLMs for tasks like summarisation, drafting, and ideation.
Content creators, marketers, and communicators interested in using LLMs for generating and refining text outputs.
Project managers, team leads, and decision-makers who need to understand the capabilities, limitations, and practical applications of LLMs.
Anyone interested in gaining practical skills to communicate effectively with AI models.
No prior experience with Large Language Models or Prompt Engineering is required, though basic computer literacy and the ability to use web interfaces are assumed.
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Participants should have:
Basic computer literacy and experience using web browsers and online tools.
While not strictly required, a basic understanding of programming concepts may be beneficial for the module covering LLM APIs and programmatic interaction examples.
No prior experience with Large Language Models or Prompt Engineering is necessary.
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This Prompt Engineering course is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
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Introduction to LLMs & Prompt Engineering
LLMs description and origin.
Interacting with LLMs (UIs, APIs, landscape overview).
Why Prompt Engineering Matters: Effective communication, unlocking capabilities.
Hands-On Lab: Exploring different LLM UIs and their capabilities.
Understanding LLM Parameters & Limitations
Tokens, context windows, and managing input/output length.
Temperature and other parameters (top-p, penalties) for controlling output style.
Understanding LLM Limitations: Hallucinations, bias, knowledge cut-off, factual accuracy.
Hands-On Lab: Experimenting with parameters to influence output.
Core Prompt Engineering Techniques
Effective Communication Principles: Clarity, specificity, constraints.
Prompt Structure: Roles (System, User), delimiters, clear instructions.
Zero-shot, One-shot, and Few-shot Prompting with examples.
Chain-of-Thought (CoT) Prompting for improved reasoning.
Role Prompting for persona control.
Negative Constraints / Anti-Prompts.
Iterative Prompt Refinement techniques.
Hands-On Lab: Practicing various core prompting techniques with diverse tasks.
Developing with LLM APIs
API Setup and Budgeting (overview).
Making Basic API Calls (using a simple script/tool).
Handling API Responses (parsing output).
Current Integration Examples.
Introduction to Function Calling / Tool Use (conceptual overview).
Hands-On Lab: Making basic API calls to an LLM model.
Prompt Engineering Use Cases & Advanced Concepts
Prompting for Personal Productivity (summarisation, drafting, ideas).
Prompting for Code Productivity (documentation, explanation, testing assistance).
Introduction to Retrieval-Augmented Generation (RAG) - conceptual overview.
Prompting for Creative Tasks.
Hands-On Lab: Applying techniques to productivity and creative tasks.
The Future of AI, Ethics, and Next Steps
Recent Developments & Coming Soon.
Threats and Opportunities.
Ethical Considerations in Prompting: Bias, fairness, safety, responsible AI.
Your Next Steps for continued learning.
Hands-On Lab: Discussion on ethical considerations, analysing biased outputs.
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Google AI Documentation: Resources and documentation for Google's AI models, including the Gemini series, relevant for understanding capabilities and API interaction. https://ai.google.dev/
OpenAI Documentation: Official documentation for OpenAI's models, including the GPT series, covering API usage, prompting guidelines, and best practices. https://platform.openai.com/docs/
Anthropic Documentation: Resources for Anthropic's Claude models, providing insights into prompting and model behaviour. https://docs.anthropic.com/
Hugging Face: A central hub for open-source AI models, datasets, and tools, offering valuable resources for exploring the wider LLM landscape. https://huggingface.co/
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