Prompt engineering remains a top-of-mind topic for nearly everyone who creates content. As large language models (LLMs) and proposal management platforms continue to evolve, people are asking an important question. How can they best leverage this technology to support the content and idea-creation process? Just as we should apply different types of technology for various jobs and outcomes, prompts are certainly not one-size-fits-all.
In this article, we go beyond the foundational aspects of an effective prompt and explore different types of prompting. The true power of generative AI lies in how effectively we communicate our requests. Understanding the various types of generative AI prompts can significantly elevate your output. Let’s explore some of these prompt types and examples and how best to use them.
1. Instructional Prompts
Instructional prompts provide clear, concise commands. The more context you provide, the more relevant your content will be. These prompts remove ambiguity, giving the AI a focused objective to achieve.
Characteristics:
- Explicit: Clearly states exactly what the AI should accomplish.
- Concise: Brief and to-the-point, avoiding unnecessary details or complexities.
- Action-oriented: Structured around verbs (write, summarize, analyze), guiding specific tasks.
Example:
“Create a 2000-word executive summary for XYZ Proposal. The document must have sections outlining the customer’s needs, our proposed solution, key benefits, and highlighting ABC company’s capabilities.”
This prompt gives the AI a clear instruction: create an executive summary. It also specifies the desired length — 2,000 words and defines the context and needs by outlining the required sections. To make this type of prompt even more effective, you can add additional instructions. For example, ask the AI to reference files that contain information relevant to the opportunity. For example, referencing the capture plan and customer research to accurately define customer needs in your generated content.
Practical Application:
- Content generation: Proposal content, Q&A, RFI content, process documentation.
- Summaries: Condensing long texts or complex topics into digestible summaries.
- Factual explanations: Providing clear, accurate explanations or definitions for educational/informational purposes.
- Structured outputs: Generating lists, outlines, reports, or instructions.
Best Practices:
- To improve the quality and relevancy of the output, use clear instructions.
- Define specific context (length, style, tone, target audience) where necessary.
- Use action verbs at the start to clearly indicate the desired output.
2. Contextual Prompts
Contextual prompts are specific instructions or statements that provide background information. They help clearly establish the scenario, perspective, or expertise the AI should adopt when responding. This approach helps ensure the AI delivers responses that are more accurate and detailed. It also makes the output more relevant and better aligned with your objectives and desired outcomes. They are similar to instructional prompts but add more context.
Characteristics:
- Clearly defined context: Specificity and providing an audience or scenario can improve the quality of generated content.
- Background info: Include information to appropriately frame the request.
- Clear constraints: Clearly state requirements or limitations.
- Task-oriented: Specify exactly what the response should accomplish.
Example:
“You are a solution engineer. Create an offering for XYZ company, highlighting our capabilities and addressing (customer need A), (customer need B), and (customer need C). The response should use technical language but cannot be overly complex. The response needs to be customer-centric and focus on how our solution will solve (problem A) and (problem B).”
Using contextual prompts helps the AI understand the exact context in which its knowledge should be applied. By clearly defining roles, settings, expertise domains, or hypothetical scenarios, you give the AI important context. This significantly enhances its performance and makes the output more relevant.
Practical Application:
- In-depth analyses.
- Expert-level explanations.
- Nuanced scenarios and discussions.
- Professional, SME, or domain-specific guidance.
Best Practices:
- Determine the specific knowledge domain or viewpoint that best matches your query.
- Clearly explain the background or role you want the AI to adopt.
- Clearly state what type of detailed information or nuance is needed.
3. Comparative Prompts
Comparative prompts ask AI to analyze and contrast concepts, products, or ideas, highlighting their unique attributes, benefits, and drawbacks. They guide AI in delivering structured and comprehensive comparisons. This enables users to understand the nuanced differences and similarities between different products or ideas. These types of prompts are especially useful for decision-making, strategic planning, and content creation.
Characteristics:
- Comparison of subjects: The prompt must ask the AI to compare at least two items. Those could be ideas, solutions, outcomes, processes, etc.
- Identification of similarities and differences: Use language that will identify areas where the two subjects are alike and different.
- Define the why: The information generated should help us see not only what is different but also why that is significant.
Example:
“Analyze the differences in (solution A) and (solution B). Identify similarities and differences and explain how each will solve (customer problem A) and (customer problem B).”
This example is a basic view of the positioning. You can also add context to focus on specific technical areas or delivery items. Try adding that context to the initial prompt and using it to expand on content during the iteration phase.
The goal is to leverage comparative prompts to objectively weigh your options and make informed choices based on detailed analysis. These prompts can be used to not only create compelling content but also to identify competitive advantages, analyze potential offerings, assess market position, and improve your offering.
Practical Application:
- Analysis and evaluations.
- Strategic and competitive assessments.
- Educational resources and training materials.
- Decision-support content.
Best Practices:
- Clearly define the criteria for comparison (cost, efficiency, sustainability).
- Encourage balanced assessments to avoid bias.
- Prompt for specific contexts or use cases to increase relevance and applicability.
4. Sequential Prompts
Sequential prompts are a strategic approach to simplifying complex tasks by dividing them into smaller, clearly defined steps. This method guides users through tasks systematically, enabling deeper dives into the most important items or concepts. This can be a great way to provide the AI with additional context by building on its knowledge.
Characteristics:
- Multi-step tasks: Useful in the creation/analysis of multi-step or complex processes or analyses.
- Step-by-step approach: The request is broken down into bite-sized chunks, allowing the AI to approach each step individually.
- Added refinement: With each step/prompt, the AI refines the output because each builds upon the last.
Example:
- Step 1: Create a summary on the past performance of XYZ company related to the delivery of (product or service A).
- Step 2: “Choose one past contract and elaborate on where we best solved for (problem A).
- Step 3: “In areas where we solved for (problem A), did we also address (problem B).
- Step 4: “In which areas did we solve both (problem A) and (problem B) most effectively and what was the positioning of the solution in that offering?”.
Here we are looking to understand how we can best position ourselves to solve the problems/address the needs of a potential customer. From our past performance, we want to identify and summarize how we have successfully addressed this in previous proposals and find our strongest qualifying performance. This is potentially a complex ask with significant context needed for a quality result. By approaching the task with a series of smaller, more clearly defined prompts, the results will be more relevant, and it will be easier to iterate or correct course if the information you receive is off track.
Practical Applications:
- Complex project management.
- Detailed instructional guides.
- Strategic planning and analysis.
- Creative ideation and refinement.
- Task delegation and oversight.
Best Practices:
- Keep the commands direct and use limited concepts in each prompt/step.
- Make sure that each step logically builds upon the previous output.
- Correct the course if the answer you receive is off-track.
5. Collaborative Prompts
Collaborative prompts are interactive inputs that encourage a dynamic, conversational partnership between users and AI. These prompts stimulate creative exchanges, building progressively refined ideas and outputs through a more open dialogue.
Characteristics:
- Open to AI interpretation: Less context on the desired output allows the AI to suggest potential approaches that may not have been previously considered.
- Context: In this case, you want to ensure the AI is aware of any nuance or additional considerations to get the most valuable results.
- Role-playing: It can help to assign a role/persona to the AI to generate content from a specific perspective.
Example:
“I want to create an RFI to assess the market and understand available solutions for (problem A) and (problem B). Suggest how I should structure the request.
AI Response: AI provides a structured outline that includes a project overview, details, information requested, and expectations for the response.
Further Interaction: Users continue refining by asking for iteration, structural changes, expansion on content/context, etc.
Through iterative feedback, writers can enhance the depth and quality of generated content. These types of prompts can be helpful for proposal development. Writers often work through a complex process that relies on iteration, feedback, and ongoing improvement.
Practical Applications:
- Content creation and refining messaging.
- Idea generation.
- Product and solution. development and enhancement.
- Scenario-based training development.
- Design collaboration.
Best Practices:
- Clearly state your initial objectives to ensure alignment from the start.
- Be specific in your follow-up questions to guide AI responses toward your vision.
- Remain flexible, allowing the collaborative process to evolve ideas beyond your initial expectations organically.
Choosing the Right Prompt
Understanding how and when to use different generative AI prompts is crucial for obtaining optimal outcomes. Always consider your end goal:
- Precision and Clarity: Instructional prompts.
- Creativity and Exploration: Collaborative prompts.
- Detailed Analysis and Insights: Contextual or comparative prompts.
- Multi-Step Tasks and Processes: Sequential prompts.
Enhancing Your Prompts
Effective prompts are clear, concise, and context-rich. Continuously refine and iterate prompts, observe AI outputs, and adjust accordingly. The better your prompts, the more powerful the generative AI outcomes will be.
Our blog on the topic provides a refresher on the elements of an effective prompt and best practices for prompt creation.
The Future of Prompts
As generative AI continues evolving, mastering prompt engineering becomes increasingly valuable. Future innovations might introduce new prompt categories, greater context comprehension, and more dynamic interactions.
By learning how to use these prompt types, you can fully harness generative AI to boost creativity, efficiency, and productivity.
What’s next?
As we mentioned in the intro, this article was the first of a two-part resource on prompting types. Check back next week for the second half of the conversation and additional examples to try.
Want to see the content created by running some of these examples? Join us for our upcoming webinar! We will share examples during the event and work through the content and iteration process in real time. Follow this link to get more information and register –
Mastering Prompt Engineering – Real World Applications and Techniques for Proposal Professionals – April 22nd, 11 am EST
After the session, all registrants and attendees will receive a file of customizable prompts to add to their resource library.
