Lesson
Maximize Your AI Output
Large Language Models (LLMs) are highly sensitive to the prompts you enter; how you interact with them and write your prompts can produce very different results. The quality of your prompt directly affects the quality of the LLMs output—this is where prompt engineering comes in.
Prompt engineering is the process of writing and refining prompts with specific context and constraints to steer the LLM's powerful "autocomplete" function toward generating the content you want. This effective strategy ensures you consistently produce accurate, relevant content and establish reliable patterns for interacting with these models in order to get your desired output.
There are many different prompt engineering techniques you can utilize when prompting your AI model. Learn more about each technique by selecting the numbered icons below.
Each AI prompt is effective in different ways. When deciding which prompting technique to use, think about what you want to accomplish. You can also combine different techniques to get the best results. Review an example of a poorly crafted prompt and an improved prompt in the Foundations of Machine Learning course on Braze Learning.
Going Beyond Prompt Engineering
Your prompt isn’t the only way you can provide information to a large language model. For more powerful applications, you can combine prompt engineering with another technique called context engineering to unlock an AI’s full potential.
Context engineering is a more advanced way of providing an AI model with information. It provides your model with all the background information, or context, that it needs to have “a full picture” before generating a response. This context can include:
- Conversation history: What has already been said
- User data: Information about the person using the AI
- External documents: Access to files or databases
- Specific rules: Guidelines on what the AI should and should not do
Imagine a marketer building a campaign for a new serum. The prompt engineering approach is straightforward: they give an AI a simple command like, "Write a 3-paragraph email for a new anti-aging serum using a professional and luxurious tone." This produces a polished but one-size-fits-all email.
In contrast, context engineering allows the marketer to build a smarter system. They would integrate customer data—like purchase history and loyalty status—and create a dynamic email template. The AI would then use all of this contextual information to generate unique messages that are relevant to each customer's individual relationship with the brand.