Gen AI Readiness, Implementation and Transformation

How to work with Gen AI in practice. Prompting, spotting errors, dealing with weird and uncanny effects, working with context, structuring reasoning and tasks, and more;

You'll learn about what Gen AI can do. The three tiers of tasks (Tier 1: Very useful; Tier 2: somewhat useful; Tier 3: dangerous!), what are the characteristics of tasks that Gen AI is good/bad at, and also what is hype versus what is true capability;

You'll learn about how Gen AI changes work, including big shifts such as most employees going from focusing on the work itself to focusing on the design of workflows (to break down tasks so some can be automated), and going from "executor" to automator (instead of actually "doing" work, focusing on the automation of tasks in the same style/format as you previously did work);

You'll learn about various technical aspects of Gen AI models. What do characteristics such as the number of parameters or the size of the context window mean, what are models for text/image/audio/video, as well as models such as multimodals or MoEs (mixtures of experts), as well as what are processes such as tokenization and vectorization for creating embeddings, why hallucinations occur, as well as an overview of what is training, fine-tuning, the creation of LoRAs, and the usage of RAG;

You'll learn about the most common pain points when adopting Gen AI in an organization, including elements such as unrealistic expectations, premature optimizations, having insufficient/unfit tools, having problems with output quality/trustability, misalignment between Gen AI and workflows, disorganization issues, various anxieties and stigmas by employees, or stochastic degration (small widespread mistakes due to hallucinations, bad Gen AI adoption, or others);

You'll learn about the right frame for using Gen AI - as a powerful assistant that needs validation - versus other unrealistic frames, such as seeing it as an oracle, a replacement, or others;

You'll learn about the basic components of prompt, from prompt engineering to agent engineering, designing quality prompts, and optimizing them for specific results;

You'll learn about the major modes of working with Gen AI - through an interface, a step in an application or an agent - and the major types of tasks it can do (generating and adapting content, distilling and structuring content, analyzing and supporting decisions, simulating and role-playing, and automating and executing);

You'll learn about structuring reasoning, breaking big tasks down into sub-tasks which can be executed with humans or Gen AI, as well as where to validate outputs and with what frequency. You'll also learn about common structures such as chains, loops and validation loops;

You'll learn about the major types of errors in outputs - hallucinations, insufficient outputs, unstable outputs and biases - as well as what causes each and how to avoid them;

You'll learn about the uncanny and weird effects of using Gen AI - "editor syndrome", feeling "centrifuged", the "Gendela Effect", and many others, how they affect you psychologically, and how to stay resilient in the face of them;

You'll learn about some key principles to actually adopt Gen AI in work, including being open-minded, experimenting, and navigating the psychological uncanniness while tolerating the inherent ambiguity and fuzziness of both the technology and workflows;

You'll learn about "problems at the joints", where individual outputs are good but they don't mesh together, including fragmentation of facts, tone and style, context, and others, cross-section inconsistencies and duplicates, and/or gradual erosion of quality;