Working with GenAI-powered Agents might seem daunting, but it doesn’t have to be. Imagine it as giving instructions to a helpful assistant—the clearer your instructions, the better the outcome. These AI-powered assistants, which we refer to as Agents, can perform a wide range of tasks based on your instructions.

That’s where prompt engineering comes in. It’s the skill of crafting clear and effective instructions for GenAI models, allowing you to get the most out of these powerful tools.

Let’s break Prompt Engineering down into its four core building blocks:

1. Setting the Stage with Alignment Instructions

Think of Alignment Instructions like a hat you want your Agent to wear, defining what you’d like them to specialize in. If you want an Agent to be your proofreader, then you don’t really need that Agent to understand anything more than how to do the best possible job at editing.

This is what Alignment Instructions do, they align an Agent to: a specific task, overall goal, desired tone (e.g., formal, friendly), and/or role.

Every time you query this Agent, it will ensure the content is thoroughly checked for grammar, spelling, conciseness, and tone. By relying on the Agent’s expertise, you can be confident that the content is of high quality and meets your standards.

2. Asking the Right Questions: Your Query

The query is the question or task you’re presenting to the Agent. The clearer and more specific your query, the better the Agent can understand what you need.

Imagine you’re asking your assistant to write a letter. You wouldn’t just say “write a letter”—you’d specify the kind of letter, who it’s for, and the tone to use.

What to do: Avoid vague or open-ended requests. Be direct and clearly state what you want the Agent to accomplish, including any specific requirements or constraints.

3. Working Within the Agent’s Short Term Memory Limits: The Context Window

The context window is the amount of information that the Agent can process at one time. It represents the Agent’s short-term memory and is typically measured in tokens, units of text the model uses to process language.

Key Point: To illustrate the capacity of a 128K context window, consider the following example:

A typical sentence in English contains around 15-20 words, with each word consisting of 1-2 tokens on average. In the context of language models, “128K” typically refers to 128,000 tokens.

This means that a 128K context window can handle around 64,000 to 128,000 words, depending on the complexity of the language used and the specific tokenization scheme implemented by the model.

To put this in perspective, a typical novel contains around 80,000 to 100,000 words, so a 128K context window can handle a substantial amount of content, potentially covering an entire novel.

Important Note: Short-term memory is a “sliding window.” Think of it like this: You can only see a few words at a time through a small window that slides across a page. As you read further, the window moves along, and you forget the words that were at the beginning. The Agent’s context window works similarly; it “remembers” recent information but might forget things from further back in the conversation.

4. Providing Key Information with Alignment Data

Agents are impressive, but they don’t automatically know everything about your specific business, products, or processes. This is where Alignment Data plays a crucial role.

Alignment Data fills in the Agent’s knowledge gaps and guides its responses. It goes beyond general knowledge and provides the context needed for the Agent to:

• Understand your business: This includes details about your products/services, internal procedures, brand voice, and customer interaction guidelines.
• Access specific information: Providing the LLM with access to documents like company policies, product specifications, technical manuals, or even internal knowledge bases.
• Tailor its responses: By providing this targeted information, you ensure the GenAI’s outputs are accurate, relevant to your needs, and aligned with your company’s standards.

Agent700 and Alignment Data

Agent700 simplifies the process of providing and managing this vital Alignment Data. Its built-in Data Library feature makes it easier to equip your Agents with the information they need.

For example, let’s say you have a document about your company’s return policy in your Agent700 Data Library. Using the “auto-type” feature, you can easily reference this document in your prompt to ensure the Agent provides accurate information about returns.

Simplifying Prompting for Future Mastery

By understanding these basic components— Alignment Instructions, Alignment Data, the Query, and the Context Window—you’ll be well on your way to crafting effective Agent prompts. Remember, practice makes perfect!