Close the GenAI Intent <> Impact Gap

Beyond hype, beyond skepticism. This is not just about learning how to use a specific tool. This is about understanding the new normal.
A masked superhero floats in the area, with a star on his chest and a fluttering cape, above the word "Expectation". Next to him, is a child wearing the same star and cape, but half the height, and standing on the ground, above the word "Reality".

Our Hot Take

There’s so much hype, so much value, so many benefits, and so many dangers. If you want your software engineering team to take advantage swiftly while mitigating the risks, the most powerful thing you can give them is a deep understanding of the benefits and risks of GenAI. Then they can use it rationally, evaluating and appropriately using new AI tooling as it is released.

Learning, with impact

Learning Outcomes: After this series of hands-on, interactive micro-workshops, engineering teams will be able to:
  • Understand what generative AI is and how it applies to their work
  • Identify good and risky use cases for different forms of AI
  • Demonstrate a deep understanding of how LLMs work and why they work the way they do
  • Develop effective strategies, competence and confidence experimenting with, and incorporating new AI tools
  • Appreciate the role of training and RLHF in the evolution of an LLM
  • Solve a well-defined problem using LLMs to generate code
  • Confidently and effectively debug code using LLMs

Session Outline

Session 1: the power and the danger (90 mins)

This hands-on micro-workshop will introduce some of the most important issues to consider when using generative AI systems, and how to systematically evaluate them. Learners will complete exercises and participate in discussions to bring these issues to life.

  • Intro to ML & AI, including different forms of AI (classification, prediction, generation).
  • Intro to the awesome power of LLMs, and the dangers of relying on them (ethical and privacy considerations, hallucinations, misunderstandings, toxicity, bias).
  • Intro to numerically evaluating LLM-based systems, including a systematic evaluation of their correctness and shortcomings.
Session 2: Under the skin of LLMs (90 mins)

This micro-workshop builds a deeper understanding of how LLMs work to enable more informed usage.

  • How an LLM is built, digging into: words as tokens / embeddings, neural nets, attention, transformers.
  • Some prompt engineering techniques, and how they relate to the LLM's structure.
Session 3: Coding with LLMs (90 mins)

A hands-on micro-workshop focused on giving Engineers the tools and awareness to effectively use LLMs as they code.

  • Coding with LLMs
  • Explaining and generating code to solve a well-defined problem (including building entire classes or modules, and building conversationally)
  • How to debug code using LLMs
Session 4: Deep Dive into LLM Tools (90 mins)

A micro-workshop that will dive into various LLM tools, how they work, and how engineers can use them effectively in day-to-day work.

  • Deeper look into software development tools, e.g. Cursor (for transforming existing code, and for debugging / fixing)
  • Agentic AI, and how it works
  • Multi-file changes with LLM IDEs
Session 5: Integrating with LLMs (90 mins)

This micro-workshop focuses on integrating with LLM tools, and mitigating their risks.

  • How to effectively integrate with LLM tools
  • Building reliable prompt engineering templates
  • Sanitising user input before it hits an LLM
  • Surrounding exemplars with escape sequences
  • Strategies for keeping humans in the loop
Session 6: CustomAIsation (90 mins)

This micro-workshop explores how to improve your LLM and how to build an effective RAG system.

  • How to fine-tune your own LLM
  • How to effectively use RAG to improve the LLM
  • Building familiarity with some typical off-the-shelf tools for RAG and fine-tuning

Coming in Q3 2025

  • GenAI Excellence for non-Engineering Teams (6 modules)
  • Building AI for Engineering Teams
Book a call to find out more

This is the future of learning