Get answers from any text with AI. Paste a context paragraph, ask a question, and the model extracts the precise answer — 100% private, in-browser.
Reading Comprehension
Recent Q&A
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AI Extracted Answer
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What is AI Question Answering?
The AI Question Answering tool is a highly advanced, local reading comprehension utility that extracts exact, factual answers directly from large blocks of text. Instead of manually skimming through long articles, legal contracts, or dry technical documentation, simply paste the text, ask a specific question, and let the local AI neural network locate the answer instantly.
Built using cutting-edge Natural Language Processing (NLP) technologies—specifically a DistilBERT transformer model fine-tuned on the SQuAD dataset—this tool understands deep semantic context. It doesn't just look for keyword matches; it reads the provided text block and intelligently identifies the exact sentence or phrase that answers your query.
How We Compare to ChatGPT & Claude
Feature
Our Local QA Tool
ChatGPT / Claude
Data Privacy
100% Offline processing
Uploads text to cloud APIs
Hallucinations
Zero (Strictly Extractive)
Prone to making up facts
Response Speed
Instant (Browser CPU)
Slow stream generation
Cost
Completely Free Forever
$20/mo or Pay-per-token
Key Features & Capabilities
100% Client-Side Privacy
Unlike commercial cloud-based AI chatbots that harvest your inputs for training data, this tool processes everything entirely within your browser using secure WebAssembly. Your highly sensitive documents and questions literally never leave your local device.
Pure Extractive QA
Generative AI models are prone to hallucinating facts. Our DistilBERT model uses strict Extractive Question Answering. This means it can only highlight the exact words from the source text you provide, guaranteeing 100% factual accuracy based strictly on your input.
Mathematical Confidence Scoring
Every answer comes with a precise calculated confidence score. This helps you determine exactly how certain the AI neural network is about its extraction, allowing you to gauge the reliability of the answer at a single glance.
Local Query History
Analyzing a massive document? The tool automatically saves your previous Q&A pairs directly to your browser's local storage. You can instantly open the history panel to review past queries and answers without re-running the heavy model.
Extractive QA: a different tool for a different job
Paste a paragraph of context, ask a factual question, and the model finds the precise answer inside that text and highlights it. The DistilBERT model (fine-tuned on SQuAD 2.0) reads your context and question together, then points at the span that answers it. The defining feature — and the reason to trust it — is that it can only return words you provided. There’s no outside knowledge to be wrong about and no creative generation to hallucinate. The answer is always traceable to a spot in your source.
What it’s great at — and what it isn’t
Works well
Doesn’t work
”When was the contract signed?"
"Is this a good contract?” (opinion)
“How many users did they report?"
"Summarize this” (use the summarizer)
“Who is the CEO?”
Questions needing outside facts
Factual who/what/when/where/how-many
Inference across many paragraphs
It answers questions with a direct, stated answer in the text. Questions requiring judgment, synthesis across the whole document, or knowledge beyond the context are the wrong fit.
Handling long documents
The model processes about 512 tokens (~300–400 words) of context at once. For longer texts, the tool splits the context into overlapping chunks and finds the best answer across them — but an answer that’s stated in one place and referenced in another can still slip through the chunk boundaries. If you know roughly where the answer lives, pasting just that section gives the cleanest result.
Where it shines
This is the right instrument for grounded answers — pulling a specific figure from a report, finding a clause in a contract, answering a comprehension question about a passage — where you need to see exactly where the answer came from. It runs entirely in your browser, so the documents you query never leave your device. For open-ended “explain or reason about this” tasks, a generative assistant is the better fit; for “find the answer in this text, and show me where,” extractive QA is purpose-built.
AI Question Answering runs its model on your own device, so the text or image you feed it never leaves the browser.
It's one of the free
AI Tools
on UseToolSuite.
Below you'll find a step-by-step guide, answers to common questions, and related tools.
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Key Concepts
Essential terms and definitions related to AI Question Answering.
Extractive question answering
A QA approach that selects the exact span of text from a provided passage that answers the question, rather than generating a new sentence. The output is always a substring of your context.
SQuAD 2.0
The Stanford Question Answering Dataset, version 2, used to train the model. Unlike v1 it includes unanswerable questions, teaching the model to abstain when the context lacks an answer.
Context window
The passage the model reads to find an answer. Very long contexts may be truncated, so keep the relevant passage focused for the best results.
Frequently Asked Questions
How does the question answering work?
The tool uses a DistilBERT model fine-tuned on the SQuAD 2.0 dataset via Transformers.js. It reads your context text, processes it alongside your question, and identifies the exact span of text that answers your question — highlighting it in the original context.
What kind of questions can I ask?
The model works best with factual questions that have direct answers in the provided text — who, what, when, where, how many, etc. It extracts verbatim answers from the context rather than generating new text. Questions requiring inference or opinion may not produce accurate results.
Is there a limit on text length?
The model processes up to ~512 tokens (~300-400 words) of context at a time. For longer texts, the tool automatically splits the context into overlapping chunks and finds the best answer across all chunks.
How is this different from just asking ChatGPT?
Fundamentally different. ChatGPT is generative — it composes an answer from everything it learned during training, which means it can answer broadly but can also confidently make things up (hallucinate). This tool is extractive — it only finds and highlights the exact span of YOUR provided text that answers the question. It can't add outside knowledge and can't invent facts, because it's literally pointing at words you gave it. Use ChatGPT for open-ended reasoning; use extractive QA when you need a verifiable answer grounded strictly in a specific document.
Why did it return no answer or highlight the wrong span?
Extractive QA only works when the answer is literally present in the context. If you ask something the text doesn't address, a well-behaved model returns 'no answer' rather than guessing — that's correct behavior, not a failure. Wrong spans usually mean the question is ambiguous, the answer is spread across multiple sentences (extractive QA pulls one contiguous span), or the context is too long and the relevant part got split across chunks. Rephrase the question to match the text's wording, or shorten the context to just the relevant section.
Troubleshooting & Technical Tips
Common errors developers encounter and how to resolve them.
The answer is wrong or empty
This is extractive QA — it finds the answer inside the context you provide and does not know facts on its own. Make sure the context actually contains the answer and phrase the question with words that appear near it.
It answers even when the context has none
The SQuAD 2.0 model can flag unanswerable questions, but short or ambiguous context can still yield a low-confidence guess. Provide a focused passage and check the confidence before trusting the span.
Related Guides
In-depth articles covering the concepts behind AI Question Answering.
