The short answer

You can use AI for RFPs without hallucinations by forcing every answer to be grounded in approved internal documents, narrowing context to only relevant sources, and designing the system to refuse answers when evidence is missing. Accuracy is achieved through governance, not by trusting the model to “be careful” or hoping the public training data helps it guess the right answer for your company.

Why hallucinations can happen in RFP workflows

Hallucinations are rarely caused by the AI being careless. They happen because the system is asked to answer questions without clear, authoritative source material.

In many organisations, knowledge is scattered across decks, wikis, emails, and outdated documents. When AI is pointed at everything at once, or at public data, it fills gaps with plausible language instead of facts. In an RFP, that behaviour becomes risky very quickly.

Common ways teams get this wrong

• Letting AI search across uncurated folders or the public internet
• Treating historical Q&A libraries as authoritative, even when outdated
• Feeding entire document libraries into the model “just in case”
• Using style prompts to mask uncertainty instead of fixing the source problem
• Reviewing answers manually without fixing the underlying knowledge gaps

What actually works in practice

The most reliable approach starts with strict knowledge boundaries. In systems like Cognaire Respond, the AI is only allowed to answer from a governed corpus of internal documents. If the information is not present, the system does not invent it.

The 'completeness' metric is provided with every AI-generated answer in Cognaire Respond. This means that the bid manager and SMEs can immediately see which answers did not fully address the original RFP question / requirement.

Context control matters just as much. A hierarchical corpus structure allows bid managers to limit the documents used for answering to a specific product, domain, topic, or document type. This prevents unrelated material from influencing the response and reduces ambiguity.

Document quality is enforced before answering even begins. New or updated documents are reviewed by AI against relevance and quality thresholds before reaching human approvers. Low quality or irrelevant material is filtered out early, so senior reviewers only see content worth approving.

Language is handled separately from knowledge. Language Rules are applied after the answer is generated, shaping tone, conciseness, and voice without altering factual content. This prevents stylistic prompts from introducing new claims.

Finally, review stages focus on learning, not just correction. When humans edit AI-generated answers, those deviations are flagged and analysed to recommend improvements to the underlying document library. Over time, the corpus gets stronger, reducing future risk.

How this differs for proposal teams and startups

For established proposal and presales teams, the challenge is scale. They already have documentation, but it varies in quality and ownership. Tight corpus governance and review workflows reduce SME fatigue and improve consistency across bids. An SME has a special 'My Questions' screen that allows them to work on reviewing answers across multiple bids in parallel. To reduce cognitive load they can also sort their assigned questions by semantic similarity, meaning that they can tackle similar questions both within a single RFP and across multiple RFPs in sequence.

For startups entering enterprise sales, a major risk is signalling maturity they do not yet have. Startups benefit from AI that refuses to guess, even if that means surfacing gaps. Knowing where documentation is missing is safer than confidently answering with incorrect claims that will be scrutinised later. Startups typically have smaller internal document sets, which allows a new corpus / document library to be created in Cognaire Respond very quickly and easily.

When accuracy matters most

Accuracy becomes critical during security questionnaires, compliance sections, and contractual responses. These answers are often reviewed by legal, security, or procurement teams who will challenge unsupported claims.

It also matters during late-stage deals, where inconsistencies across answers can undermine trust. At that point, speed matters less than being defensible under scrutiny.

A closing perspective

AI can draft RFP answers faster than humans, but speed is not the hard problem. The hard problem is knowing when not to answer. Systems that prioritise governance, evidence, and refusal over fluency are the ones that avoid hallucinations in practice.