Think of it like giving instructions to a new team member—the clearer, more specific, and more actionable the instructions, the better the results.

As mentioned in the Agent Builder Bob section, Bob is an expert in crafting Agents, including their prompts. It’s recommended to work with Bob in order to at least get the foundation of the prompt built. Working with Bob to establish a strong foundation for the prompt can save time and effort. From there, you can tweak it manually as needed or continue refining it with Bob's help.

Key Elements of a Good Prompt

A strong prompt should include the following elements:

1. Objective – Define what the Agent should accomplish.
2. Process – Outline the steps the Agent should follow, including how to retrieve and structure the output.
3. Important Notes – Key guidelines to keep in mind while performing the task.
4. Example Query (Optional) – SQL or data queries to guide the Agent in retrieving accurate information.

1. Objective

Start by clearly defining what it is that you want the Agent to accomplish. Be specific about the task to ensure focused results.

• Example: "Objective: Benchmarking. Compare key metrics of a given advertiser against the average of those same metrics across all accounts in the same industry."

2. Process

Break down the task into organized steps that guide the Agent toward the correct output. The more complex the task or the more datasets involved, the more important it is to define clear step-by-step instructions—just as you would when training a human.

• Example:
  • Process:
    • Step 1: Locate the given advertiser name in the Google Ads dataset. If the user has not given you an advertiser name, ask for one.
    • Step 2. Extract Cost and Clicks for the given individual Advertiser for the last 30 days. If that advertiser does not have data in the last 30 days, inform the user of that.
    • Step 3. Then, identify all accounts that are associated with the same industry group that the individual account is associated with, using the Account_Groups dataset.
    • Step 4. Extract Cost and Clicks for all those accounts that have data in the last 30 days. Not all of these accounts may have data in that timeframe. That’s ok.
    • Step 5. Calculate the group average Cost per Click (Sum of Cost for all accounts DIVIDED BY Sum of Clicks for all accounts)
    • Step 6. Compare the individual account’s performance against the group benchmark. Present your findings in a table format where there are 2 rows: 1. [Individual Account Name] 2. [Industry Account Group Name Average] and present your calculations for each. Formatted table example below:
      | Comparison | CPC |
      |---------------------------|-----------------------|
      | [Individual Account Name] | $3.50 |
      | [Industry Account Group Name] | $2.80 |

3. Important Notes

Include any guidelines the Agent should keep in mind while completing the task. Repetition helps AI—just like it does with humans.

• Example:
• Important:
  • Make sure you’re always analyzing the last 30 days worth of data, unless otherwise instructed by the user.
  • Check your work before presenting the results to the user.

4. Sample Query (Optional)

For tasks requiring precise data retrieval, you can provide an example SQL query. This helps the Agent understand how to extract relevant information.

• Example:
• Query to Execute:
  SELECT "Cost", "Clicks"
  FROM "Google Ads-Campaign Performance"
  WHERE "Advertiser Name" ILIKE '%Nike%';

Tips for Crafting Effective Prompts

• Be specific, not vague. Instead of saying, “Analyze data,” specify what data to analyze and how through actionable steps. Describe what kinds of insights you’re looking for in the output. If the Agent is given clear instructions on what you’re looking for, the output will be more successful.
• Anticipate outputs: Specify how you want the results to be presented (e.g., downloadable CSV, charts, or a bulleted list for easy copying into an email).
• Start simple: Begin with straightforward tasks. Then test and validate that the results are correct and in the desired format. Build complexity as you refine the Agent.
• Iterate and test: Crafting prompts is an ongoing learning process—test and tweak to get the best results.

Agent Builder Bob is trained to generate prompts in the above format. Instead of starting from scratch, leverage Bob’s expertise by chatting with him in the ‘Create’ tab. Let him do the heavy lifting and get the foundation of the prompt set up for you. From there, you can tweak and make manual edits as needed.

A well-structured prompt is the foundation of a high-performing AI Agent. Whether you build it yourself or let Agent Builder Bob handle the heavy lifting, refining your prompts will ensure better, more reliable AI-driven results.

External Resources

AI prompt engineering is evolving rapidly, with new strategies emerging regularly (e.g., zero-shot, few-shot, chain-of-thought). Staying up to date with best practices can help you build even more effective AI Agents in NinjaCat. Here are some great resources to explore:

OpenAI Prompt Engineering

Prompt Engineering Guide