Responding to RFPs (Requests for Proposals) drains time and resources - especially for high-volume, complex bids in B2B SaaS and technology. But in 2025, AI tools like ChatGPT for RFP responses are transforming how modern teams handle RFPs at scale.
In this complete RFP ChatGPT guide, you’ll learn:
- How different ChatGPT and Claude models compare.
- How to set up Custom GPTs, Projects, or Gems - and why they matter.
- Exact instructions you can reuse to automate sales RFPs.
- Real prompts to extract questions and avoid AI hallucinations.
- The hidden pitfalls: hallucinations, context leaks, version control headaches.
- Why a specialised RFP AI platform like Vera exists to solve these problems.
Who is this guide for?
If you’re a proposal manager, bid manager, or RevOps lead at a B2B SaaS or tech company handling high-volume, complex RFPs or security questionnaires, this guide will show you how to leverage ChatGPT for RFP automation to improve speed and accuracy - and free up your team for strategic work that closes deals faster.
What is ChatGPT and How Does it Help with RFPs?
ChatGPT is a Large Language Model (LLM) that generates text, answers questions, and processes complex documents. In RFP workflows, it can:
- Extract questions from PDFs, Excel sheets, or questionnaires.
- Draft answers from your uploaded documents.
- Highlight gaps, inconsistencies, or missing info.
- Speed up repetitive tasks that usually drain your experts’ time.
Comparing LLMs for RFP Automation
Not all GPT models (or other LLMs) are equal. Here we compare OpenAI’s ChatGPT versions, Anthropic’s Claude::
| Model | Strengths | Weaknesses | Best For |
|---|---|---|---|
| GPT-3.5 | Fast responses and low cost per query make GPT-3.5 a good choice for simple tasks. It’s widely available and works well for short prompts, quick drafts, and repetitive admin checks. | Limited reasoning ability compared to newer models means it struggles with multi-step logic or complex dependencies. Can hallucinate more if prompts aren’t specific. | Best for straightforward RFP questions, short standard answers, low-risk internal drafts where accuracy is easy to review. |
| GPT-4 | More accurate than GPT-3.5 with better understanding of nuance and context. Handles moderate complexity well. Good balance of speed and cost for most teams. | Slower response times than 3.5. Higher cost means it’s not ideal for high-volume, repetitive low-value tasks. | Reliable for general RFP workflows, standard proposal answers, checking compliance points, drafting boilerplate sections with moderate customisation. |
| GPT-4o & o3 | OpenAI’s latest models with stronger reasoning, improved logical flow, and better multi-step problem solving. Handles dependencies across long sections of an RFP more effectively. | Still new - can be inconsistent in how it handles very niche instructions. May require more prompt testing to get reliable results every time. | Ideal for more complex RFP logic: pricing justifications, technical Q&A, security clarifications where clear step-by-step reasoning is needed. |
| Claude Instant | Lightweight version of Claude, designed for quick tasks at a low cost. Fast at summarisation and simple question extraction. | Lower accuracy and weaker performance on large, detailed files. Struggles with deep context or highly technical detail. | Good for basic question extraction from PDFs or short questionnaires, quick summaries of simple requirements. |
| Claude 3 | Anthropic’s flagship model with longer context windows - can read, remember, and reason over larger documents (e.g. big security questionnaires). Good at nuanced answers, code blocks, or JSON output. | Less flexible for custom instructions compared to OpenAI’s Custom GPTs. Fewer third-party integrations. | Strong for security questionnaires, vendor due diligence packs, coding-heavy responses, or reviewing long compliance attachments. |
| Claude Projects | Lets you group instructions, files, and multiple threads in one ‘Project’. Keeps some context and reusable instructions in one place for a specific RFP. | Same base model hallucination risks apply - plus there’s a risk of context leaks between chats in the same Project. Limited audit trail means consistency can still drift. | Best for teams who want to store big files and reuse consistent instructions for multiple RFPs without starting from scratch every time - but still need to watch for inconsistencies. |
Some teams experiment with Gemini for live fact-checking or web-connected research, but for core RFP workflows, GPT and Claude remain the most practical.
Key takeaway: GPT-4o and GPT-o3 outperform others for RFP logic and reasoning tasks. Claude 3 is strong for coding-heavy sections or massive doc summaries.
Creating a Custom GPT or Project for RFPs
Within ChatGPT, you can create:
- Custom GPTs: Save instructions, upload files as context.
- Projects: Similar, but better for managing multiple threads under one umbrella.
Claude calls them Projects, Google Gemini calls them Gems - they all work the same way: you pre-define reusable instructions and context.
How to set it up:
- Gather your latest approved RFP answers, policies, product specs.
- Use OpenAI’s Custom GPT tools to upload docs and instructions.
- Test with real questions: Does it generate accurate drafts?
- Keep your Custom RFP GPT updated to avoid outdated answers.
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You can create custom GPTs here: Create a Custom GPT
Give it the following instructions:
This GPT extracts and answers RFPs and complex security questionnaires for SaaS, technology, and professional services companies.
It reads and processes all uploaded documents fully to identify questions and existing answers without skipping rows, pages, or sections.
It uses only the uploaded context and does not guess or fabricate information; if critical data is missing, it responds with NO_DATA.
It drafts clear, precise, and professional answers using structured bullet points when appropriate, maintaining consistency with the company’s known
security, compliance, and technical details from the uploaded documents.
It flags unclear areas for human review and can prepare outputs in Excel- or Word-compatible formats for submission readiness.
Here is a Pre-made GPT you can copy:
https://chatgpt.com/g/g-686e440e49388191afb959ad5f4164df-vera-s-rfp-response-assistant-template
Real-World Prompts to Automate Sales RFPs with ChatGPT
When using Chat GPT you are real, copy-paste prompts you can try now to speed up your next proposal.
Extracting questions
Extract all questions from this RFP PDF and return them in a numbered list.
Draft standard answers
Draft an answer for Question 5 using our uploaded product spec and security policy.
Check for compliance
Check this draft RFP answer against our ISO 27001 standard. Highlight any gaps or risks.
Cross-check with knowledge base
Cross-reference this answer with our latest security FAQ and confirm consistency.
Draft clarifications
List any unclear or missing requirements in this RFP and draft clarifying questions for the issuer.
These practical examples make RFP ChatGPT workflows actionable - so your team saves hours on repetitive tasks.
Practical Issues & Limitations
Using ChatGPT for RFPs has huge upsides - it’s fast, flexible, and good at repetitive drafting. But it’s worth remembering: GPT models are trained for general-purpose use and simple Q&A, not the highly structured, detail-critical world of complex RFPs and security questionnaires.
By default, GPT does not “understand” how your documents are organised, which sections matter most, or how to handle sensitive compliance nuances. It will often skip sections, invent answers, or make assumptions if instructions aren’t clear enough.
Workaround: You can partially fix this by writing explicit, detailed prompts for every new file - spelling out the format, tabs, sections, and rules for when to say “NO_DATA.” But this manual approach depends heavily on your team remembering every detail every time - and applying instructions consistently.
Better solution: If you want to avoid these risks altogether and make AI truly dependable for RFPs and security questionnaires, it’s smarter to wrap GPT inside a specialist tool like Vera - adding the guardrails, audit trails, and version control that general-use chatbots just don’t offer.
Here’s where the biggest pitfalls show up - and how to handle them:
A) Extracting questions in different formats
RFPs rarely arrive clean. PDFs, Excel sheets, merged cells - GPT will guess if you don’t spell it out.
Tip: Always provide explicit file instructions:
- Name the file and format.
- List every tab, table, or section.
- Define exactly which rows or columns contain questions vs. answers.
Be explicit about the file structure. For example:
“This is an XLSX file named Vendor_Security_Questionnaire.xlsx. It has 3 tabs: Tab 1: Instructions – this tab contains only instructions and can be safely ignored. Tab 2: Security – this tab contains questions in Column A, answers in Column B, and comments in Column C. Tab 3: Compliance – this tab also has questions in Column A, answers in Column B, and comments in Column C.
If the document is an RFP or security questionnaire with sections, clearly identify and name each section. For example:
“This is a PDF file named ACME_RFP_July.pdf. It contains 5 clearly marked sections: Introduction Pricing Technical Requirements Security Requirements Terms and Conditions
If the uploaded document has sections in between rows or changes in structure, you must explicitly call this out before extracting. For example: “This is an XLSX file with 2 tabs: Tab 1: Security This tab contains sections in between rows with section headers in Column A (e.g., General, Technical, Privacy). Questions are listed under each section header in Column A, with answers in Column B and comments in Column C. Continue extracting questions and answers under each section until the next section header appears. Clearly state the section name for each extracted question. Tab 2: Compliance This tab contains three separate tables within the same tab: Table 1 (rows 1–15): Questions in Column A, Answers in Column B. Table 2 (rows 17–30): Questions in Column B, Answers in Column C. Table 3 (rows 32–50): Questions in merged cells across Columns A-B, with answers in the row directly beneath each question. State explicitly which table and row range each question came from.”
B) Hallucinations & “lazy reading”
Large Language Models love shortcuts - they’ll skim the first few rows, assume the structure, and skip the rest. They can fill gaps by making up details, or rephrase negative answers to sound positive.
To solve this you can try to write a prompt such as:
You must not invent or infer any details that are not explicitly found in the uploaded documents. If the information required to answer a question is not present, unclear, or incomplete, return NO_DATA rather than guessing or fabricating a response. Always ground your answers strictly in the content provided and explicitly state when something is not found.
AI also tends to be too optimistic, often saying that you’re working on something or trying to avoid saying you don’t have or meet a requirement
To fix this you can try to write prompts such as:
You must not attempt to sound positive or optimistic if the requirement is not met. If a question in the RFP asks for a certification, process, or feature that is not present in the uploaded documentation, you must respond with NO_DATA. Do not rephrase missing or negative answers to sound softer or more optimistic. Be direct, factual, and neutral.
C) Collaboration & activity logs
GPT alone does not keep a log of who uploaded a file, who approved an answer, or who changed instructions. Without version control or shared oversight, different people can upload conflicting files or run separate threads - and no one has a reliable record of what changed, or why. This is how duplicate work and mixed answers creep in.
D) Knowledge base & context
GPT Projects can “peek” at other threads in the same Project - which means it might quietly reuse contradictory context from unrelated RFPs. Because all context must be manually uploaded, it’s easy for critical files to be forgotten or go stale. There’s no tagging or categorisation - so you can’t organise by product, region, or version - and no way to exclude outdated context or set granular approvals. The result: inconsistent or conflicting answers with no clear source of truth.
How Vera Solves These Gaps
These limitations are exactly why Vera exists. Vera wraps ChatGPT for RFPs in a structured, secure workspace built for real teams:
- One shared source: Everyone works from the same live knowledge base - no disconnected Projects.
- Always-on knowledge base: Approved content stays current and consistent across answers.
- Clear permissions: Upload, tag, and manage files with control and context - so nothing goes stale or gets mixed up.
- No hidden context leaks: Vera’s workspace makes context transparent and reusable only when and where you want it.
- Full audit trail: See exactly who uploaded files, who reviewed changes, and who approved final answers.
- Better extraction: Vera handles complex formats, multi-tab Excel sheets, and messy PDFs - reliably and repeatably.
- No manual resets: Vera keeps your knowledge reusable and consistent — so you don’t have to start over or re-upload files for every new RFP.
5-Minute Demo: Automate Complex RFPs & Security Questionnaires with Vera
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FAQs
Q: What is RFP ChatGPT?
A: It’s using ChatGPT to help automate tasks in the RFP process - from extracting questions to drafting answers.
Q: Can I automate sales RFPs with ChatGPT?
A: Yes - but pairing ChatGPT with a central workspace like Vera ensures accuracy and consistency at scale.
Q: How do I stop ChatGPT from hallucinating in proposals?
A: Use clear instructions, a strong knowledge base, and always verify AI-generated text.
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