# 3. AI Features

### 1) Knowledge Base Answers

The bot uses a RAG system (Retrieval Augmented Generation):

It searches your server’s knowledge base for relevant entries. If it finds relevant content, it answers using that content. If it finds no relevant entries, it will not guess. It will respond that it doesn’t have information.

This prevents the bot from answering random or unrelated questions.

#### 1) Context Awareness (Short conversation memory)

The bot remembers a small amount of recent conversation history so it can handle follow-up questions more naturally (example: “what about step 2?”).

#### 2) Feedback Learning (👍 / 👎)

After the bot replies, you can react: 👍 if the answer was good 👎 if the answer was incorrect or unhelpful

This feedback is recorded and used by the bot to improve future answers.

#### 3) Usage + Performance Analytics

Admins can view analytics with: `/analytics`

This includes: Knowledge base usage User activity Feedback stats (positive/negative) Performance metrics


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://ansari-labs.gitbook.io/ansari-labs/products/ai-powered-assistant/setup-guide/3.-ai-features.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.