AI assistants helping with RFP response process

How to Use ChatGPT and Claude to Streamline Your RFP Response Process: A Practical Guide

Published on January 28, 2025

While there is a lot AI won't replace in proposal and grant writing, it can dramatically speed up the routine work and even be a partner to work through more knotty issues. Here's some practical tips on how to use AI assistants effectively in your RFP process.

1. Converting Boilerplate Content

If you have a content library of past responses you can use those to generate tailored responses to new questions. Here's an example prompt:

Here is our previous response to a similar question:

[paste previous response]

Here is the new question:
[paste new question]

Please create a new response that:
- Uses active voice
- Maintains specific examples and credentials
- Adapts the content to exactly match the new question
- Uses a professional, solution-focused tone

If you don't have a content library already but do have some past responses, you should take a look at our guide to creating an intelligent RFP library.

2. Making Generic Content Specific

A common complaint about AI-generated content is that it can sound robotic and generic. Here's how to get more specific responses:

Please enhance this response by:
- Adding specific methodologies we use: [list them]
- Including relevant metrics from our past performance: [add metrics]
- Referencing our actual tools and processes: [list them]
- Using industry-specific terminology for [industry]

This is also somewhere that I would recommend using certain specific AI models for their better writing performance. If you are using Claude, you should consider using their Opus model, which is specifically designed for writing. Or you could take a look at Google's Gemini 2.0 model which also excels at writing compared to the ChatGPT models.

3. Brainstorming Ideas for Tricky Questions

One of the most valuable but overlooked uses of AI is as a brainstorming partner for difficult RFP and Grant questions. Modern AI models like Claude-3 and GPT-4 can help explore different angles and approaches, especially for questions where there's no clear "standard" response.

This is where I would typically reach for a reasoning model such as ChatGPT o1 or OpenRouter. The strength of these models is that they're able to think through complex problems systematically, often leading to more robust and creative responses.

Setting Up the Brainstorming Session

The key is to give the AI enough context to think through the problem systematically. Here's an effective prompt pattern:

I'm working on an RFP response for [type of client] and need help brainstorming approaches for this question:
[paste question]

Our company's relevant experience includes:
[list 2-3 key experiences]

Our main differentiators are:
[list differentiators]

Please:
1. Break down the different aspects we should address
2. Suggest 3-4 different approaches we could take
3. For each approach, note potential strengths and weaknesses
4. Identify any proof points we'd need to support each approach

Example: Technical Innovation Question

Let's say you're facing this question: "Describe how you would introduce innovative technologies while ensuring operational stability."

Rather than jumping straight to an answer, use AI to explore different frameworks:

For this technical innovation question, please help me think through:
1. What are the key tensions/tradeoffs we need to address?
2. What different frameworks could we use to structure the response?
3. What concrete examples would strengthen each approach?
4. What risks should we acknowledge and address?

Tips for Effective AI Brainstorming

If you use one of the reasoning models mentioned above you will get a lot of this behaviour for free. However, if you want to use a more standard model you can use these tips to get better results from your brainstorming session:

  1. Start Broad, Then Narrow: Begin with wide-ranging exploration, then use follow-up prompts to focus on the most promising approaches.
  2. Use "Chain of Thought" Prompting: Ask the AI to explain its reasoning step by step. This often surfaces interesting considerations you might have missed.
  3. Challenge the AI's Assumptions: When it suggests an approach, ask it to identify potential weaknesses or counter-arguments. This can lead to more robust responses.
  4. Connect to Past Successes: Have the AI help you identify relevant examples from your past work that could support each approach. This is where your intelligent RFP library can be of assistance.

Example Dialogue

Here's how a brainstorming session might flow:

Here's a tough question from an IT services RFP: "How would you ensure knowledge transfer while maintaining service continuity during team transitions?"

Let's break this down:

Key Components:

  1. Knowledge transfer processes
  2. Service continuity requirements
  3. Team transition management
  4. Risk mitigation

Possible Approaches:

  1. Phased Transition Focus

    • Strengths: Clear, methodical, easy to understand
    • Weaknesses: Might seem too conservative
    • Needed Proof: Past successful transitions

  2. Technology-First Approach

    • Strengths: Modern, scalable
    • Weaknesses: Might seem impersonal
    • Needed Proof: Specific tools and results

  3. People-Centric Model

    • Strengths: Addresses client anxieties about team changes
    • Weaknesses: Could seem light on technical details
    • Needed Proof: Team retention metrics, client testimonials

Which approach aligns best with your experience and the client's priorities?

When to Use AI Brainstorming

This technique is particularly valuable for:

  • Questions without obvious "standard" answers
  • Requirements that seem to conflict
  • Areas where you need to differentiate from competitors
  • Complex technical or process challenges

Remember: AI brainstorming is about generating ideas and approaches, not creating final content. Use it to explore possibilities, then apply your expertise to shape the winning response.

Best Practices from the Field

Here are some final points to bear in mind when using AI to respond to RFPs:

  1. Always Double-Check Claims: AI can hallucinate or make incorrect assumptions. Never submit AI-generated content without careful review, especially when answers will be unique to you or the client.
  2. Use AI as a colleague: Think of AI as a team member - good at some things but not perfect. It's particularly useful for reviewing content or quickly generating answers to common questions. But do work with it as it goes through the process.
  3. Don't skip the human touch: Winning proposals comes from understanding what really matters to the client - information AI tools often won't have. Remember to pay attention to client relationships, pre-bid meetings, and your own research etc.

Looking Ahead

The RFP response landscape is evolving rapidly. We're seeing:

  • Increased scrutiny of AI use, especially in government contracting
  • New tools combining AI with proposal management
  • Growing emphasis on transparent AI use

The key is finding the right balance - using AI to handle routine tasks while maintaining human oversight of strategy and quality. As one person put it: "AI doesn't replace good proposal writing - it just helps good proposal writers work faster."

By following these guidelines and maintaining clear processes, you can effectively integrate AI into your RFP workflow while avoiding common pitfalls and maintaining proposal quality.