Prompt intelligence vs. prompt engineering: Understanding the differences

Generative artificial intelligence (GenAI) applications can sometimes feel opaque and confusing. Given the black-box nature of large language models (LLMs), users often find it challenging to connect the dots between the prompts they input and the responses they receive.  

Nonetheless, the enterprises building GenAI-powered applications, and the users of those applications, want effective AI interactions. These interactions should be:

• Stable: The same prompt or input should produce largely the same response. 
• Robust: Slight alterations to the input should not drastically change the behavior of the application. 
• Reliable: The output produced by the application should be largely accurate, with little to no traces of misinformation. 
• Explainable: The user should be able to deduce how the output was generated, either through an explanation provided by the application or through sources cited by it.

In this quest for effective AI interactions, two techniques have gained considerable traction: prompt intelligence and prompt engineering. Although these terms are sometimes used interchangeably or confused with each other, they are completely different processes, each with its own distinct set of considerations. Prompt intelligence and prompt engineering also have distinct roles to play in optimizing interactions with GenAI applications. 

Laying the groundwork with definitions 

Both prompt engineering and prompt intelligence are relatively new concepts within the GenAI space. They’re also rapidly evolving as GenAI technology matures and adoption widens.

Prompt engineering is the process of crafting specific inputs to guide AI outputs with a focus on precision and control. In other words, prompt engineers design highly specific and verbose instructions for a GenAI application to follow to elicit a desired output reliably.

Prompt intelligence is the process of continuously analyzing user prompts and application responses in order to refine and enhance the application’s accuracy, relevance, and efficiency. It also helps application builders better understand how users are leveraging the application and how well the system actually helps them achieve their goals.

Keep these high-level definitions in mind as we take a closer look at each. 

What is prompt engineering? 

Prompt engineering is the process of crafting and refining prompts to elicit desired responses from AI models. 

Although the most commonly used AI models (such as GPT-4o, Gemma, Mistral, and Llama 3) are adept at filling in missing context, they perform best when given clear, verbose, and specific instructions. Depending on the complexity of the task you want the AI to execute, your prompt should contain one or more of the following components:

• Primer: This guides the AI in how it should behave, such as a role or perspective to adopt. 
• Instructions: Clear instructions specify what the AI needs to execute. For more complex tasks, it helps if the instructions are broken down into smaller, sequential steps. 
• Input: This is the data that the AI needs to work with. 
• Output format: The output format specifies how the AI should respond. 
• Examples: These include possible inputs, correct outputs, and perhaps even incorrect outputs, which the AI can use to better understand the request.

To demonstrate why prompt engineering is important, imagine an ecommerce enterprise with customer data, including purchase history, stored in a structured SQL database. We want non-technical employees, such as marketers, to ask questions in natural language to extract information from the database.

To build this system, we would need a phase in the process where the LLM generates an SQL query that answers the question asked. Here are two examples of this using ChatGPT:

In the example above, we’ve given very little instruction on what needs to be done, and ChatGPT has attempted to fill the gaps wherever possible. The output that you see here is not necessarily usable for the following reasons: 

• It’s difficult to parse the SQL query from the response reliably. 
• The table and column names may not exactly match our database schema. 
• The LLM might be using a flavor of SQL that is not compatible with the database we’re using. 

Contrast the above attempt with the following interaction:

This interaction is much more reliable, stable, and robust. We’re extremely clear on what database we’re using, the columns in our table, and the output we expect from the AI. 

We could try running both prompts multiple times. In all likelihood, the second prompt will produce the same correct output every single time, while the first prompt will produce a wide range of varying responses. 

Basic prompt engineering techniques include the following guidance: 

• Be specific in your ask. Instead of saying, “Create a detailed market research report,” prompt the AI with more instructions such as number of words, the sections the report should contain, or specific areas to highlight. 
• Provide context about the task. Tell the AI what needs to be done, how its response will be used, and other details that will prevent it from filling in the gaps. This will go a long way toward ensuring it behaves in the desired way. 
• Incorporate roles. Tell the AI to adopt an archetype upon which to model its behavior. For instance, if you want to review a legal document, ask it to take on the role of an attorney at an international law firm. 
• Use clear and concise language. Be clear in what you want. If possible, break down the task into simpler steps that are easy to perform. Remove any element of vagueness in your language. 
• Give examples. For more complex tasks, providing examples of proper responses is often the best way to teach the AI how to perform the task correctly. 
• Specify the format. Clearly define the expected format for the AI’s response to ensure it fits seamlessly into your workflow. 
• Iterate and refine. You often won't get your prompt right on the first try. Prompt engineering may require several iterations before arriving at correct and stable outputs. 

What is prompt intelligence? 

With systematic and effective prompt engineering, you’ve managed to reliably elicit correct responses from your GenAI application. Will this always translate directly to a successful application?

Unfortunately, the answer is no. This is because it’s often difficult to predict how users will interact with your application. You may often find that actual user prompts differ dramatically from the example prompts that came out of your prompt engineering efforts. Or perhaps your users are using the GenAI application for purposes entirely different from what you originally intended or expected.

Prompt intelligence is essential for these reasons. It is only by analyzing user prompts and AI responses that you can achieve AI model optimization to improve future interactions.

By continuously refining your application based on the feedback you receive, you ensure that the accuracy and relevance of AI responses improve. Those refinements may take the form of underlying model fine-tuning, application enhancements, or providing better prompt-crafting guidance to your users.

Prompt intelligence also yields insights into how your GenAI application is actually being used (versus what you may have envisioned). This gives enterprises more clarity on the application's potential ROI, helping them identify the best opportunities to increase performance.

Ultimately, prompt intelligence increases user satisfaction by continuously improving AI interactions to be more consistent, clear, and dependable. 

How prompt engineering and prompt intelligence work together 

Prompt engineering and prompt intelligence are neither in opposition to each other nor act as alternatives. The two techniques are complementary. To build a successful AI application, both should be used together.

The insights you gain from prompt intelligence can better inform prompt engineering practices. Engineered prompts, in turn, can be continuously deployed and monitored, generating valuable data for prompt intelligence. In this way, both techniques work together to create a cycle of continuous improvement.

Again, consider the database-backed GenAI system from our earlier example. This time, let’s assume that the application fields questions from marketers by accessing a repository of PDF documents.

In the first iteration of prompt engineering, you may provide concise information to your end users. However, through prompt intelligence, you may discover that your users find it hard to trust the application’s answers and take them at face value.

To refine your application, you can direct the AI to provide additional context for its responses, always including the data source (such as the PDF location, page number, and line number). This refinement would drastically increase your users’ trust in the application. 

Empowering enterprises to build optimal GenAI applications 

Prompt engineering focuses on designing instructions that generate correct and stable outputs from GenAI applications. On the other hand, prompt intelligence is concerned with insights into how users and AI interact with one another and the value it creates. Enterprises pursuing GenAI innovations can use both techniques together to build applications with effective AI interactions that score high in user satisfaction.

When it comes to concerns around data privacy or the effectiveness and trustworthiness of GenAI applications, Outshift by Cisco is the authoritative resource. As your enterprise leans on Outshift through its GenAI journey, continue your learning with the following helpful resources: 

• The Breakdown: What is a large language model (LLM)? 
• AI infrastructure: How to prepare your organization for transformation 
• 6 steps to assess AI readiness in your enterprise