Testing shows that supplying large language models with only verified Q&A pairs rather than full knowledge bases increases high-quality RFP answers by 24%. In a 133-question benchmark, ChatGPT produced 4.5% verbatim, 9.8% usable, and 57.1% incorrect responses, demonstrating the importance of context engineering and structured prompts with confidence ratings. Controlled retrieval and human review remain essential for accuracy and intent alignment. When these methods are applied, average RFP completion time drops from 25–40 hours to about 6 hours, highlighting measurable efficiency gains from disciplined AI use.
Responding to a Request for Proposal (RFP) is a demanding task. It requires meticulous attention to detail, extensive collaboration, and a significant investment of time. The pressure to deliver a high-quality, winning proposal is immense. So, can AI help?
The short answer is yes. Generative AI is transforming the RFP process, offering a powerful way to enhance efficiency and improve the quality of your responses. Tools like ChatGPT and specialized platforms like Arphie are changing how teams approach this critical business function.
This guide shows how AI can transform the way you respond to RFPs, helping sales engineers, solutions engineers, and pre-sales teams streamline their workflows and win more deals, faster.
For all AI models, it starts with the controllable inputs you give to the model. How to optimize the content you give for the highest quality results is a studied field called context engineering. Context engineering includes two important components: the prompt, and the attachments as added context. The goal is to select the smallest, highest-signal set of inputs that most effectively guide the model’s reasoning and behavior while avoiding “context rot” from excessive or irrelevant information. Context engineering is really all about treating context as a scarce resource and designing how that resource is allocated to produce the high quality answers to RFPs.
It begins with a clear and disciplined prompt. Think of this prompt as your instructions to a junior colleague: outline the task, provide the tools they need, and define how the output should be structured. Here’s a well-structured prompt to use.
“You are assisting me in completing an RFP. I will provide you with a list of RFP questions in spreadsheet format. For each question, find the best possible answer using the reference documents I provide. If an exact match exists, copy it verbatim. If no exact answer exists, generate the most accurate possible answer based on context and best judgment. For each answer, provide a confidence rating (Verbatim, High, Medium, Low). Output the results in a CSV file with columns for Question, Answer and Confidence. Be precise, professional, and avoid filler. Each answer should be ready to send back to the RFP issuer.”
The second part is the other context you attach. When using base models like ChatGPT or Claude directly, gather only your validated Q&A pairs. Resist the temptation to upload every piece of content you have; large language models weigh all context equally, making it harder to distinguish critical information from noise. A focused context set improves accuracy. To quantify this, we tested giving Perplexity, Claude, ChatGPT, and Gemini all of our company’s knowledge base (e.g., unstructured documents, verified Q&A pairs) versus just giving the verified Q&A pairs, and just providing verified Q&A pairs as context was 24% better on average for the verbatim and high quality answers.
Models unfortunately don’t do well with a lot of context right now. Think of the context window as a desk with a fixed size. You can only fit so much information on it at once. If you try to pile on too much, like flooding the desk with papers, you lose sight of what’s actually important. Some models even have smaller desks, meaning you have to be extra selective about what information you include so the model can make the best, most informed decision.
This is precisely why purpose-built RFP platforms take a different approach; they connect to your entire knowledge base but use intelligent retrieval and filtering to present only relevant context to the model for each specific question, avoiding the context pollution problem that plagues direct model usage.
The third step is to submit your prompt and context to your preferred model provider. For sake of comprehensiveness, we tested against the 4 most common model providers today: Open AI’s ChatGPT (GPT-5 Pro), Claude’s Sonnett 4.5, Perplexity, and Gemini.
ChatGPT is great at well-structured, direct questions.
For instance, ChatGPT can easily handle questions like “How many employees do you have?”. This is a clear question where ChatGPT can easily infer the question relates to the question in our Q&A library “company size” and respond verbatim with the correct response.
However, it’s important to note that ChatGPT needs the proper context or else it will quickly hallucinate. For instance, in a similar type of question “what year were you founded”, the Q&A library didn’t have an answer. As a result, the model hallucinated saying, sharing our Recovery Time Objective instead.
ChatGPT also excels at pulling from broad, industry‑standard practices. For instance, a question in our RFP was “what makes your organization a stable, long-term business partner?” ChatGPT is able to pull on its generalized knowledge about what makes a good business partner, and then tailor that to Arphie giving the response:
“Arphie is built for long-term partnership on three pillars:
(1) Financial & Organizational Stability – Privately held, founded in 2023, headquartered in San Francisco, and backed by institutional investors with a multi-year capital plan.
(2) Sustainable Product Strategy – Our roadmap is grounded in customer-driven priorities, with continuous investment in our core strengths: tagging, inline citations, and iterative source decomposition to ensure high-quality RFP responses.
(3) Operational Excellence & Compliance – We maintain SOC2 compliance, robust data governance, and a transparent product update cadence to ensure our platform evolves reliably without service disruption. Customers choose Arphie not just for software, but for a partner that continuously adapts to their needs, supported by a team dedicated to long-term success.”
ChatGPT can struggle with questions that are specific to your product because it only sees a limited amount of information at a time—the “context window.” That means it doesn’t actually know your company or product beyond what you include in that window. Unlike humans, it can’t rely on intuition or prior experience to fill in gaps.
For broader questions, like “what makes a good company?”, ChatGPT performs much better because it can draw on general knowledge learned during training. But for product-specific questions, it needs enough focused, high-quality context to understand your company’s details before it can give accurate answers.
AI‑generated answers tend to fall into three types of mistakes: the model misunderstands the question itself, the model fails to grasp the intent behind the question, or the model adds irrelevant information that isn’t needed:
When we tested ChatGPT against a 133‑question RFP, its performance underscored both the potential and the limitations of current models. ChatGPT delivered perfect answers for 12% of the questions, and needed only minor touch-ups for 17.3% more. The vast majority of responses were problematic: 42.1% of ChatGPT generated responses completely misunderstood the question, 21% of ChatGPT generated responses misinterpreted the intent behind the question, and 7.5% of ChatGPT generated responses added irrelevant information that wasn’t needed.
The takeaway is that while using ChatGPT is not yet ready to answer your RFPs end to end, it is a valuable starting point. It can accelerate the drafting process and surface useful context, but every answer still needs a human eye. On the bright side, as models evolve, particularly in their ability to parse intent, you can expect those error bars to shrink.
While ChatGPT is often the default choice, other models offer distinct strengths for answering RFPs. Anthropic’s Claude, Perplexity AI and Google’s Gemini can provide alternative first drafts and perspectives.
Here’s a pros and cons list of each model put together after extensively testing the model against ~100 RFPs:
Arphie was born out of frustration and personal experience. Dean, Co-Founder and CEO, spent countless hours at Scale AI responding to RFPs while leading a software product line, while Michael, Co-Founder and CTO, faced the same challenge at Asana as an infrastructure engineering leader—losing valuable engineering time answering repetitive questions as a subject matter expert. After testing already-available tools and watching them miss the mark on most questions, and talking to countless customers of these existing tools, they realized that legacy solutions didn’t work because they relied on rigid, static Q&A libraries. At the same time, AI was just emerging as a promising solution—but generalized language models struggled with the level of context and specificity RFPs require. So they started from the ground up: designing purpose-built AI agents around the real workflows of sales engineers and proposal teams.
The result is a platform that delivers 76% better answer quality than an off-the-shelf, generic tool like ChatGPT. Here’s how.
Integrate with your company’s knowledge base. Arphie begins by connecting directly to wherever your knowledge lives. Whether your content lives in Google Drive, SharePoint, Salesforce, Highspot, Seismic, Confluence or even Front, the platform ingests every document, PDFs, spreadsheets, presentations, wiki pages, and keeps itself up to date whenever a file changes. There is no need to copy or manually manage a separate library; if your product team publishes a new white paper or revises a policy, Arphie knows about it immediately. By eliminating duplication and constant maintenance, it frees your team to focus on actual proposal work rather than needing a full-time content librarian.
Start with perfect matches, then widen the net. If the question you’re answering already exists in your Q&A library, Arphie returns the verbatim answer you’ve vetted, just like the legacy tools you’re used to. But when there isn’t a direct match, our AI agents spring into action. Because we’ve indexed your knowledge base, we can go beyond canned text and look across all relevant sources to craft an accurate response. That dual mode, library first, AI second, ensures speed when possible and depth when necessary.
Patent‑pending chunking. Arphie’s document‑processing pipeline doesn’t simply vectorize entire pages and hope for the best. We break every file into contextually dense, temporally connected, and modally homogenous components that fit inside a model’s context window. For each question, our system parallelises across these chunks and identifies which components merit “full attention” relative to the query. It then synthesises a coherent answer from the selected snippets and highlights exactly which pieces of text contributed to the response. Throughout the process, programmatic and probabilistic checks, such as token‑level log‑likelihoods and text‑level heuristics, help mitigate hallucinations. This chunk‑gather‑synthesise‑validate loop produces answers that are both comprehensive and grounded in your source material, rather than relying on brittle distance calculations over embeddings.
Tagging for precise context. Every document ingested into Arphie can be tagged by any custom categories your team wants. When an RFP asks about the “expense platform,” the AI restricts itself to documents tagged accordingly, bypassing irrelevant content about other products. This granular filtering is most helpful to companies with multiple product lines or regional nuances, where a generic answer could blend information that should remain distinct. Tagging ensures the AI searches only within the right context.
Confidence levels and interactive review. Some questions simply can’t be answered with high certainty because the data isn’t there. Arphie never fabricates confidence. Answers are clearly marked as verbatim, high, medium or low confidence, allowing sales engineers to prioritize their time. Every response comes with citations; clicking on a citation opens the original document in context so you can verify the language or pull additional details. If the AI misses the mark, you can change the wording of the question and re‑generate an answer, translate the response into another language, or enforce a word count limit. The system is designed for iteration.
Designed for collaboration and flexibility. A proposal rarely involves just one person. Arphie’s collaboration tools let you bring in colleagues who own specific features or policies and assign them as reviewers on a question. Because Arphie’s pricing is per project rather than per seat, you never have to ration licenses; invite everyone necessary to get the job done right.
What does this all result in? High quality answers delivered in a fraction of the time. Across our user base, customers switching from legacy RFP or knowledge software typically see speed and workflow improvements of 60% or more, while customers with no prior RFP software typically see improvements of 80% or more. This time savings is attributed to a high quality first draft composed of verbatim answers from the Q&A library, and high-quality AI-generated answers that typically only require minor edits.
As a case study, one customer who migrated from a legacy RFP platform has completed 269 RFPs since joining Arphie, a robust sample size. Their team's acceptance rates tell the story: 3% verbatim from Q&A library, 84% of AI-written responses accepted as-is, 7% requiring minor edits, 4% major edits, and only 2% unanswered. Assuming the industry standard of 15 minutes per question and subtracting actual editing time, this represents a savings of 19 hours per RFP, bringing typical completion time down to 6 hours. Some customers average 3-4 hours.
These numbers are pulled directly from our database, not marketing fluff. To put them in perspective: industry surveys show RFPs take 25-40 hours on average (roughly 15 minutes per question for a 100-question RFP). Even using Loopio's own "42% faster" claim generously, that brings completion time to ~17.5 hours. Arphie's 6-hour average for this customer represents a 2.83x improvement over legacy tools and 4.16x improvement over using ChatGPT directly, driven by integrated knowledge base access, intelligent tagging, and patent-pending chunking.
When evaluating AI tools, pay close attention to your security posture. Your data is your moat, and it should never be used to train someone else’s model. Submitting proprietary information to consumer AI platforms can unintentionally expose or teach those systems your confidential knowledge. Always review and adjust data-sharing settings to keep your information private and ensure it isn’t incorporated into public or shared models.
Arphie complies with SOC 2 Type II standards, which is the highest level of security to protect your customer data from getting stolen. We also have a zero data retention policy, meaning that your data is your data. We and our model providers will never use it to train our own model, rather we only use your data to produce the highest quality answers for you. If you ever want to remove a document, you have granular access to remove documents at the file or folder level. Because our CTO worked at Palantir before founding Arphie, it is baked into the DNA of Arphie to be security first on all fronts.
AI tools offer tremendous leverage but cannot yet replace human expertise. Think of them as smart junior sales engineers: they can draft responses, surface relevant context and speed up your workflow, but they rely on you to provide direction, correct mistakes and fill knowledge gaps. With a disciplined prompt, a curated context set and a platform built for your workflow, AI becomes a force multiplier. The power lies in knowing how to delegate and where to intervene.
RFP automation uses artificial intelligence to streamline the proposal response process. It works by ingesting your company's knowledge base, matching RFP questions to existing Q&A pairs, and generating answers with confidence ratings. When exact matches exist, the system returns verbatim responses; when they don't, AI generates contextual answers based on your documentation.
Use this structured prompt: "You are assisting me in completing an RFP. I will provide you with a list of RFP questions in spreadsheet format. For each question, find the best possible answer using the reference documents I provide. If an exact match exists, copy it verbatim. If no exact answer exists, generate the most accurate possible answer based on context and best judgment. For each answer, provide a confidence rating (Verbatim, High, Medium, Low). Output the results in a CSV file with columns for Question, Answer and Confidence."
In testing with 133 questions, ChatGPT provided perfect verbatim answers for only 4.5% of questions and usable first drafts for 9.8%. It completely misunderstood 57.1% of questions, misinterpreted intent in 21% of cases, and added irrelevant information in 7.5%. ChatGPT requires proper context to avoid hallucinations and struggles with company-specific queries.
Enterprise RFP automation should comply with SOC 2 Type II standards, maintain zero data retention policies, offer encryption at rest and in transit, provide granular document-level access controls, and support audit logging. For regulated industries, verify GDPR, CCPA, and industry-specific compliance certifications.
Common failure points include: (1) Poor context management—uploading too much irrelevant content, (2) Lack of validated Q&A library—starting without curated answers reduces accuracy, (3) Insufficient human review—trusting AI outputs without verification, (4) Resistance to workflow change—teams continuing manual processes, (5) Inadequate training—not teaching teams how to refine prompts and review AI outputs effectively.
Arphie leads with AI-native architecture designed around sales engineering workflows, offering 10x better answer quality than base models through patent-pending chunking, tagging-based context filtering, and integrated knowledge base management. Evaluate vendors based on: library-first + AI-second approach, confidence scoring, inline citations, collaboration tools, and content repository integration.
