Try Before You Build: Running an AI Proof of Concept on Your Own Documents
The most useful AI proof of concept is not a build. It is a small sample of your own documents, a chat set up on top of them, and a handful of the people who will actually use it asking their own questions. In a few weeks it shows which documents are ready, what users really ask, and what is worth building.
Key takeaways
- The most useful first step in a RAG project is finding out whether your documents and your users are ready for it.
- A small sample of real documents shows format, version and coverage problems a demo never will.
- Real users ask questions no project team would write — and those questions are your best test data.
- Different roles judge the same answer differently, so test with more than one of them.
- A proof of concept turns "we should build a RAG" into a specific, defensible plan.
Most retrieval-augmented generation (RAG) projects start with a build plan. The more useful ones start with a question: will this actually work on our documents, for our people? The fastest way to answer it is an AI proof of concept on a small sample of your own content, used by the people who will depend on it — before anything is built.
Why test before you build?
A team we worked with came to us planning a RAG system so their staff could ask questions of internal knowledge and get answers with sources. The plan was sensible. The order was not.
We suggested a different first step: do not build anything yet. Share a small sample of your documents, let us set up a chat on them, and let your real users try it.
Here is what that looked like:
- Their documents, not ours. The team shared a sample of their actual documents and described the people who would rely on them.
- Answers with sources. We set up a chat on those documents, with every answer pointing to the document it came from.
- Real users, real questions. People from different business roles used the chat and asked whatever they wanted.
- With and without tailoring. We showed the results with the model as it comes and after it was tailored to their content, so they could see what the extra work changed.
- A right-sized plan. We then worked with them to shape a solution sized to what the test showed: which documents, which users, and how much tailoring.
The most useful first step in a RAG project is finding out whether the documents and the users are ready for it. You can only find that out on your own content.
What does a proof of concept on your own documents show that a demo cannot?
1. Your documents behave differently from demo documents
Demo content is clean. Real knowledge is a mix of formats, versions, scanned pages and out-of-date copies. A test on your own sample shows which documents are ready and which need work, before you commit to indexing thousands of them. It is the same gap behind most AI pilots that never reach production: the pilot proved the technology, not the content.
2. Real users ask questions the project team would never write
Project teams test with the questions they expect. Users ask in their own words, about their own tasks, often in half a sentence. Those questions are the best test data you will collect, and you only get them by putting the system in front of users.
3. Different roles judge answers differently
An operations user wants the answer fast. A compliance reviewer wants the source and the exact wording. A finance user wants the figure to match the approval. A test with one type of user hides all of this.
4. You learn what to build before you build it
A proof of concept turns "we should build a RAG" into a specific list: which sources matter, who can see what, which questions need the most care, and what "good" looks like. That list is a much stronger basis for a decision than a feature comparison.
How do you run your own AI proof of concept?
- Start small. A small sample from one area of the business, not everything.
- Choose content you are comfortable sharing. Agree how it will be handled before anything is uploaded.
- Pick real users. Two to four people from the roles that will use it, not only the project team.
- Let them ask freely. Do not script the questions, and keep a record of everything they ask.
- Agree success criteria first. For example: answers are correct, the source is shown, and users would use it again.
- Review together. The questions that failed tell you what to fix before you build.
What should you look for in the results?
Three things matter more than an overall impression.
- Coverage. Sort every question into answered well, answered badly, and not covered by the documents. The last group is not a failure of the system — it is a map of what your knowledge base is missing.
- Sources. Check that each answer points to a document that actually supports it. A cited answer your reviewers can verify is worth far more than a fluent one they cannot.
- Access. Make sure answers only draw on documents each person is allowed to read. On a real rollout this is the hardest part of enterprise RAG, and a proof of concept is the cheapest place to see it working.
From proof of concept to a decision
At the end, the decision is simple because the evidence is specific: build, extend the test, or stop. If you build, you already know which documents to start with, which roles to design for, and where tailoring the model is worth the effort — the question explored in RAG vs. fine-tuning.
Not sure whether your organization is ready for a test like this? The AI Readiness Scorecard takes a few minutes and shows where to start.
Frequently asked questions
What is an AI proof of concept for RAG?
How many documents do we need to share?
Who should take part?
What should we decide at the end?
Test it on your own documents before you build.
In a walkthrough we set up SphereIQ on a small sample of your content and show cited answers to the questions your team really asks.