Most teams already have the answer somewhere. The problem is that it is buried across PDFs, handbooks, research papers, policies, and internal documents. Search returns pages. A generic chatbot guesses. Sharper turns those files into a knowledge base you can browse, inspect, and question.
The key difference is grounding. Sharper answers from the knowledge base you select, shows the source context behind the answer, and surfaces related concepts so users can keep exploring instead of accepting a black-box response.
In this walkthrough, we upload four files: two language-model research papers, the BASIS student handbook, and a sample employee handbook. Then we ask practical questions across the corpus and verify the answers against the original documents.
What is knowledge base question answering? It lets users ask natural-language questions across uploaded files and receive answers grounded in those documents, with source context and related concepts available for verification and follow-up.
The setup: four files, one knowledge base
The demo knowledge base contains four very different PDFs: BERT, RoBERTa, BASIS.pdf, and SampleEmployeeHandbook.pdf. That mixture matters because real teams rarely have one tidy source. They have research, policies, school handbooks, HR documents, and product files living side by side.
Step 1 - Upload files to build the knowledge base
The user uploads the four PDFs and imports them into Sharper. Sharper parses the files, indexes the content, and prepares the knowledge base for browsing and question answering.
Four uploaded files become one searchable knowledge base.
How Sharper works, in two moves. First, you build a knowledge base: upload your documents once and Sharper parses and indexes them into a reusable, searchable collection. Second, you ask: start a new task, select that knowledge base, and ask any question in plain English - as in the steps below. Sharper answers only from the selected knowledge base, so every response is grounded in your documents.
Step 2 - Browse documents, concepts, and graph views
After import, the user can browse the knowledge base in three complementary views. The document view shows each uploaded source. The concept view shows topics Sharper extracted from those files. The graph view maps how documents and concepts connect.
Document view: every source file is visible and ready for inspection.
Concept view: Sharper extracts topics from the uploaded knowledge base automatically.
Graph view: documents and concepts become an explorable map.
Step 3 - Click a concept to explore the topic
Concepts are not just tags. Each one has a summary, description, and source mentions. For example, the ParentSquare concept is summarized as the online portal for all school communications, payments, and the family directory.
Click a concept to see what Sharper knows and which source mentions it.
This makes the knowledge base useful even before a user asks a question. They can browse the topic map, inspect a concept, and jump back to the source that supports it.
Step 4 - Select the knowledge base and ask a question
Once the knowledge base is selected in the chat composer, the user can ask a direct question:
The question: "How many paid vacation days for the first year?"
Ask in plain English after selecting the knowledge base.
Sharper searches the selected knowledge base, finds the vacation policy in the employee handbook, and answers in context: full-time employees earn 2 weeks, or 10 days, of paid vacation during the remaining nine months of the first year, with no vacation earned during the first 90 days.
The answer is grounded in the source PDF, with the relevant passage visible beside it.
The answer also includes related concepts. Click one, such as Equal Employment Opportunity Policy, and Sharper opens a concept panel with a summary, description, and source mentions. That turns a single answer into a path for exploration.
Suggested concepts help users explore new topics without losing the answer context.
More Q&A examples from the same knowledge base
Because the knowledge base contains both research papers and handbooks, Sharper can answer very different questions while still grounding each response in the right source material.
Research question: Sharper explains how RoBERTa builds on BERT.
Policy question: Sharper answers with citations from the BASIS student handbook.
Built for large document collections
The same workflow scales beyond four files. Sharper is designed for knowledge bases with thousands of large PDFs, with individual files up to 200 MB. Users can still browse the corpus, inspect concepts, explore the graph, and ask questions against the selected knowledge base.
That scale matters for teams working with employee handbooks, school policies, research libraries, legal archives, support documents, and operational manuals. The answer should not depend on someone remembering which PDF contains the rule.
What makes Sharper different
- Grounded in the provided knowledge base. Sharper answers from the files the user selected, not from generic model memory.
- Source context reduces hallucination. Answers are tied to retrieved passages, and users can inspect the original PDF context.
- Concepts make answers explorable. Related concepts help users move from one answer into the surrounding topic area.
- Multiple views support different workflows. Documents, concepts, and graph views let users browse before or after asking questions.
- Scale without losing precision. Sharper can handle thousands of large PDFs while still finding answers in context.
FAQ
What kinds of files can Sharper use for a knowledge base?
Sharper can work with large PDFs and common document formats, including research papers, student handbooks, employee handbooks, policy documents, manuals, and internal knowledge files.
How does Sharper reduce hallucination?
Sharper searches the selected knowledge base, grounds answers in retrieved passages, and shows source context so users can verify where an answer came from.
Can users explore topics instead of only asking questions?
Yes. Sharper builds document, concept, and graph views. Users can click a concept like ParentSquare or Equal Employment Opportunity Policy to see summaries, descriptions, and source mentions.
Does Sharper scale beyond a few demo files?
Yes. Sharper is designed to handle thousands of large PDF files, with uploads up to 200 MB per file, while still returning grounded answers from the selected knowledge base.
Can Sharper answer questions across unrelated files?
Yes. A single knowledge base can contain different document types, such as research papers and handbooks. Sharper searches the relevant source material for each question and answers in context.