A policy chatbot needs the source document

A general chatbot answers from the internet

A chatbot trained on general text knows what leave policies usually say. Your policy is a different thing. Ask a general model how many sick days you get and you'll hear an average, stated with confidence.

Retrieval changes the order of work. A retrieval bot searches your own documents first, finds the passage that matches the question, and writes its reply from that passage.

So your answers can only be as current and as clear as the passage behind them. That's why the setup starts with the documents, not the bot.

The document stays the source

Your policy is what HR wrote, approved and can defend. A chatbot reply gives you a short summary of it. When the two ever read differently, the document wins.

So a good setup keeps the document close to the answer. In PeopleMuster every policy has its own page, with a category and a summary, inside the same app your team uses for leave and attendance.

Your employee reads a quick reply from HashBot, then opens the full policy to check the exact wording before booking time off. Nobody has to hunt through a shared drive for the latest file.

What publishing a policy does in PeopleMuster

HR writes and edits policies on an authoring screen that only HR and admin roles can open. When you publish, PeopleMuster splits the text into chunks and indexes them for HashBot to search.

Your authoring screen shows how many chunks each policy was indexed into, next to who edited it last and when. You can see at a glance that a new policy is ready to answer from.

You have no model to retrain. Edit the policy, publish it again, and HashBot searches the new wording. HashBot runs on PeopleMuster's OpenAI integration, and suggested questions help your team get started.

Write policies a bot can quote

Retrieval rewards plain writing. A rule buried in the fourth paragraph of a long document is hard to find for a person and for a search index alike.

Short passages that each state one rule give your bot a clean piece to quote. They also give your employee a clear sentence to read when they open the policy.

• Keep one topic per policy, so leave and conduct never share a page.
• Put the number in the sentence that states the rule.
• Name each leave type the way your team asks for it.
• Replace an old version when you publish a new one, so two rules never compete.
• Use headings that read like the questions people ask.

Send the decisions to a person

Some questions aren't policy questions at all. "Can I take Friday off?" depends on your manager and your team's week, not on a rule.

HashBot explains the rule. Your leave request flow handles the decision, with an approver and a record. Keeping those two apart stops a chatbot reply from reading like a yes.

When the policies don't cover a question, your employee raises it on the internal helpdesk under the Official Policies category. HR gets a ticket with a number, and your employee can follow its status.

Watch those tickets. A question people raise every month is a policy you haven't written yet.

How to test any policy chatbot

Run these checks in a demo before you trust a policy assistant with your team's questions. Each one tests the document behind the bot, which is where the quality comes from.

• Change a rule, publish it, and ask about it straight away.
• Ask the same question in two different wordings.
• Open the policy behind an answer and compare the wording.
• Check who can edit the source, and whether the screen shows the last edit.