Why LLMs Should Help You Think, Not Write Your Requirements List

Generative AI has found a natural home in the software evaluation process. Buying teams are using it to research vendors, summarize product documentation, and increasingly, to generate requirements lists before engaging the market. The appeal is obvious. A well-prompted LLM can produce a detailed, organized, professionally worded list of software requirements in under a minute. It feels like a significant head start.

The problem is that what comes out is not really your requirements list. It is an industry requirements list that happens to have your company name on it.

What an LLM actually produces when asked for requirements

When you ask a language model to generate software requirements for, say, a content management system or a CRM platform, it draws on an enormous breadth of information about how those categories of software are typically evaluated. It knows what procurement teams generally look for, what analysts typically recommend, and what feature sets appear most frequently in RFPs across the industry.

The result is a document that is long, detailed, and largely correct in the abstract. It will include integration requirements, security and compliance considerations, scalability expectations, user access controls, and reporting capabilities. A vendor receiving it will recognize every item. They have answered variations of these questions hundreds of times.

What the document will not contain is anything specific to why your company is evaluating software right now, what has broken down internally, what a successful outcome actually looks like for your team, or what constraints make your situation genuinely different from the industry norm. Those things do not exist in the training data. They exist in your organization.

Why this creates problems for the evaluation

Sending a generically thorough requirements list to vendors creates a specific kind of problem: it invites generic responses. Vendors are experienced at responding to RFPs and requirements documents. When the requirements look familiar, the response process looks familiar too. You receive polished answers to questions you did not really need answered, and the unique aspects of your situation never surface.

It also creates internal confusion. A requirements list generated from industry context will often include capabilities that are not relevant to your actual situation, at a level of specificity that implies you have already done analysis you have not done. Teams can spend time debating requirements that have no bearing on the actual decision.

Perhaps most importantly, a long list of generic requirements shifts the evaluation toward feature-matching rather than problem-solving. You end up scoring vendors on how many boxes they check rather than how well they understand your situation and can address it.

Where LLMs actually add value in this process

The more productive use of AI in a software evaluation is not requirements generation. It is requirements articulation. There is an important difference.

Your team almost certainly knows what its challenges are. The people closest to the work understand what is broken, what is slow, and what the consequences are. What is often missing is the ability to express those challenges in language that is clear, specific, and useful for vendor conversations. That is where a language model can genuinely help.

Rather than asking an LLM to generate your requirements, bring your own context to it. Describe the problem your team is experiencing in plain language. Explain the business impact. Share the internal constraints. Then use the model to help you articulate that more precisely, identify dimensions of the problem you may not have fully considered, and structure it in a way that is clear for an external audience.

The output of that process is something a vendor cannot easily produce a templated response to. It reflects your actual situation, in your language, grounded in your specific goals and constraints. That is a fundamentally more useful starting point for an evaluation.

A more effective prompt approach

The difference often comes down to how you engage the tool. Compare these two approaches:

A prompt like "generate a requirements list for a marketing automation platform for a mid-market B2B company" will produce a competent, generic document.

A prompt like "we are a mid-market B2B company with a 12-person marketing team. Our current platform requires manual list management that takes two days per campaign and causes us to miss timing windows. Our sales team says lead quality from marketing has dropped over the past two quarters but we do not have clear visibility into why. Help me articulate these challenges clearly and identify the questions we should be bringing to vendors" produces something your organization actually owns.

The second prompt treats the LLM as a thinking partner rather than a document generator. The output reflects your reality, not the industry average.

What to bring to vendors instead

A one to two page problem statement built from your own internal context, refined with AI assistance, will outperform a five page requirements list built from industry templates in almost every evaluation. It gives vendors something genuine to respond to. It creates space for the best pre-sales teams to ask follow-up questions and build something tailored. And it keeps the conversation anchored to what actually matters for your business.

The buying teams that get the most out of vendor evaluations are not the ones who arrive with the most complete requirements documents. They are the ones who arrive with the clearest understanding of their own situation.

AI can help you get there. Just not by writing the list.

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Nick Robinett is a Senior Solutions Consultant with 8+ years of experience in enterprise SaaS pre-sales. He writes about the buying and selling experience from the pre-sales perspective.