Wrap Up

Let's review some of the key takeaways covered in this course.

Key takeaway #1

Generative AI is a branch of artificial intelligence that creates new content. It uses large language models to analyze training data and predict the next most likely token in a sequence to produce realistic outputs, such as images, text, or audio.

Marketers can use generative AI to scale content production, but they must leverage the tool effectively to avoid robotic outputs and instead to forge genuine connections through enhanced personalization.

Key takeaway #2

Large Language Models (LLMs) are highly sensitive to the prompts you enter, and how you write your prompts can be the difference between a high-quality result and a generic or vague output. Prompt engineering is one way to improve AI-generated content quality. It can include different techniques such as assigning a persona, using chain-of-thought reasoning, and applying few-shot examples. Each technique serves a distinct purpose, and they can be combined to achieve the best results.

While effective prompting is essential, an AI’s full potential is truly unlocked when paired with context engineering. This incorporates conversation history, user data, external documents, and specific rules to provide the model with a more complete understanding of the task.

Key takeaway #3

A reasoning model is a type of AI that aims to emulate human reasoning to generate answers, and it is expected to check its work without constant human oversight. These models work with clear objectives and break down goals into smaller, manageable pieces. They use tools to perform a wide range of actions, retrieve information beyond their original training data, and retain memory to store context and past interactions, which helps them avoid repeating mistakes.

What’s next

In the final course of the AI Fundamentals Learning path, you’ll learn about reinforcement learning, specifically:

• How reinforcement learning works and how it differs from traditional supervised learning.
• How the explore-exploit tradeoff in reinforcement learning.
• How to identify strategies for solving multi-armed bandit problems, including Epsilon Greedy and Epsilon Decay.
• The concept of a contextual bandit and its application in decision-making.
• How to recognize and categorize customer features relevant to marketing strategies.
• Environmental features that can impact marketing decisions.
• How the role of action features in reinforcement learning and marketing contexts.
• How action features can help reinforcement learning agents drive more personalized experiences.
• the advantages and disadvantages of contextual bandits compared to multi-armed bandits.

Once you've completed this path, you'll be ready to take the Braze AI Fundamentals Certification exam to validate your knowledge on the topic. Learn more about the Braze AI Fundamentals Certification on Braze Learning.