Question Answering API, Based on Generative AI

What is Question Answering?

Question answering is about letting the AI automatically answer a question. Optionally, you can give some context to the AI model to help it answer the question. Generative AI models like ChatGPT, GPT-3.5, GPT-4, LLaMA 2, Yi 34B, and Mixtral 8x7B are very good at performing question answering.

For example imagine you want to ask the following question:

How to bake some bread?

The AI might answer something like this:

1. Grease a clean, dry bread pan with butter.
2. Stir 1 envelope dry active yeast to 1/4 cup warm water in a large bowl. Let the yeast bloom for 10 minutes, or until dissolved, then add 1 teaspoon salt, 1 teaspoon honey, and 1/2 cup unsalted butter.
3. Add 3 1/2 cups strong flour and mix well, then wait to process your dough for 3 minutes.
4. Add an ounce of water and mix well. If the water was not already warm enough to make a dough (i.e., it was not warm to the touch) then add a warm to hot bowl of water to the machine. Stir continuously for 3 minutes.
5. Turn your dough out onto a clean sheet of plastic wrap and fold over.
6. Cover with another piece of plastic wrap and place in a warm corner of your kitchen.

Now maybe you have specific advanced data you want to give the AI and ask a question on it (also known as "context"):

All NLP Cloud plans can be stopped anytime. You only pay for the time you used the service. In case of a downgrade, you will get a discount on your next invoice.

You might want to ask the following question:

When can plans be stopped?

And the answer would be:


You might also want to answer questions about a large corpus of internal domain knowledge. In that case you will want to read our dedicated article about semantic search + generative AI (also known as RAG): read it here.

Question answering

Why Use Question Answering?

Question Answering can be usefully used in the "real world". Here are a couple of examples.

Contracts Questions

Chat bots are used more and more everyday, both to answer customer questions and internal collaborators questions. Imagine that a customer is asking a legal question about his contract. You could perfectly use a question answering model for that and pass the contract as a context.

Product Questions

Here's another chat bots related example. Imagine that a collaborator has a technical question about a product. Why not provide him with a natural language interface and make his life easier?

Healthcare Consultation and Diagnosis Support

Generative AI can assist doctors and healthcare professionals by providing quick and accessible medical consultations or diagnostic support. By analyzing symptoms and medical history input by the user, AI can generate a list of possible conditions and suggest the next steps for treatment or recommend seeing a specialist. While not a replacement for professional medical advice, it can serve as a valuable tool for preliminary consultation, especially in underserved regions with a scarcity of healthcare providers. Additionally, it can assist medical professionals by staying up-to-date with the latest research and medical guidelines, thereby enhancing the quality of care.

Educational Tutoring and Assistance

In the educational sector, generative AI can serve as a personal tutor, providing students with explanations, additional study resources, and tailored feedback on their work. For subjects ranging from mathematics to language learning, AI can adapt to the student's learning pace and style, offering personalized question-answering sessions that can clarify doubts and explain concepts in different ways until the student understands. This could democratize access to personalized education, making high-quality educational support accessible to students regardless of their geographical location or financial means.

NLP Cloud's Question Answering API

NLP Cloud proposes a question answering API that allows you to perform question answering out of the box, based on advanced models like Deepset's Roberta Base Squad 2, LLaMA 2, Mixtral 8x7B, Yi 34B, and more. These models are very good alternatives to ChatGPT, GPT-3.5, and GPT-4. The response time (latency) is very good for the Roberta model and the accuracy of generative models on this task is very impressive. You can either use the pre-trained model or train your own model, or upload your own custom models!

For more details, see our documentation about question answering here. For advanced usage, see the text generation API endpoint here. And easily test question answering on our playground.

Testing question answering locally is one thing, but using it reliably in production is another thing. With NLP Cloud you can just do both!

Frequently Asked Questions

How does generative AI work in question answering systems?

Generative AI in question answering systems works by using models that have been trained on large datasets to predict and generate text-based answers based on the input question. It analyzes the context and semantics of the question, then synthesizes a response that aligns with the learned information, essentially simulating human-like responses.

What are the main differences between rule-based and generative AI question answering systems?

Rule-based AI question answering systems rely on a set of predefined rules and logic to generate answers from a fixed set of information, making them more limited in scope and adaptability. In contrast, generative AI systems use machine learning models to understand and produce answers dynamically from a vast pool of data, enabling them to create more nuanced and contextually relevant responses.

Can generative AI understand context in a conversation?

Yes, generative AI can understand context in a conversation to a certain extent, by analyzing the sequence of words and utilizing trained models to infer meaning. However, its understanding is limited to patterns in data it has been trained on and may not fully grasp nuance like a human.

How does generative AI deal with ambiguous questions?

Generative AI typically handles ambiguous questions by leveraging the context available within the input data and its trained models to infer the most probable answer or generate multiple plausible responses based on patterns learned during training. If ambiguity remains, it may produce answers reflecting the uncertainty or ask for clarification.

What are the limitations of question answering with generative AI?

Generative AI is limited by its reliance on pre-existing data, which may lead to outdated or biased answers, and it struggles with understanding context deeply or accurately interpreting ambiguous or highly specific queries. Additionally, it can generate plausible but factually incorrect answers, termed "hallucinations."

How can bias be managed in generative AI question answering systems?

Bias in generative AI question-answering systems can be managed by training the models on diverse, balanced datasets and implementing algorithms that can identify and mitigate biased patterns or outputs. Additionally, continuous monitoring and updating of the model, alongside ethical guidelines and human oversight, play crucial roles in minimizing bias.

How to evaluate the accuracy of question answering?

To evaluate the accuracy of question answering, one commonly uses metrics like precision, recall, F1 score by comparing the system's answers to a set of known correct answers (ground truth). Additionally, human evaluation is often conducted to assess the quality and relevance of the answers, considering nuances and complexities not captured by automated metrics.

What languages does your AI API support for question answering?

We support question answering in 200 languages

Can I try your question answering API for free?

Yes, like all the models on NLP Cloud, the question answering API endpoint can be tested for free

How does your AI API handle data privacy and security during the question answering process?

NLP Cloud is focused on data privacy by design: we do not log or store the content of the requests you make on our API. NLP Cloud is both HIPAA and GDPR compliant.