AI · alternatives · 2026
24 alternatives to Question Extractor: turn text into LLM finetuning data
Generate question/answer training pairs out of raw text. - nestordemeure/question_extractor
Question Extractor is a tool that converts text into training data suitable for language model finetuning. Available on GitHub, it automates the process of generating question-answer pairs from source material, helping… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Question Extractor: turn text into LLM finetuning data launched in 2023; newer entries below may have overtaken it.
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I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…
2023 · github.com · its alternatives →
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Question Base▲453Turn Slack into a knowledge base with generative AI
2023 · questionbase.com · its alternatives →
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- 4WS
We’ve trained a generative AI model to browse the web and answer questions/retrieve code snippets directly. Unlike ChatGPT, it has access to primary sources and is able to cite them when you hover over an answer (click on the text to go to the source being cited). We also show regular Bing results side-by-side with our AI answer. The model is an 11-billion parameter T5-derivative that has been fine-tuned on feedback given on hundreds of thousands of searches done (anonymously) on our platform. Giving the model web access lessens its burden to need to store a snapshot of human knowledge…
2022 · beta.sayhello.so · its alternatives →
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Generate questions from any text or topic
2023 · opexams.com · its alternatives →
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Improved file parsing for LLM’s. Contribute to Filimoa/open-parse development by creating an account on GitHub.
2024 · github.com · its alternatives →
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2017 · deepquiz.com.s3-website-us-east-1.amazonaws.com · its alternatives →
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LLM SEO FAQ▲275Generate search intent optimized FAQs for any URL for FREE
2025 · llmseofaq.com · its alternatives →
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I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
2024 · embedding.io · its alternatives →
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Learn how to build semantic search systems. From machine transition to question-answering.
2021 · pinecone.io · its alternatives →
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Questgen.ai▲238Generate quizzes from any text in one click using AI
2023 · questgen.ai · its alternatives →
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One of the coolest things I've been able to get GPT-3 to do is generate questions based on a piece of text. So, I built a simple site to help others do the same. Hopefully, this can be useful to teachers, trainers, or just anyone who wants to create a fun and unique quiz.
2022 · quizgecko.com · its alternatives →
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We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
Mar 2026 · github.com · its alternatives →
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A little idea I got from playing with AI SWE Agents. Can AI help make sure we understand the code that our AIs write? PR Quiz uses AI to generate a quiz from a pull request and blocks you from merging until the quiz is passed. You can configure various options like the LLM model to use, max number of attempts to pass the quiz or min diff size to generate a quiz for. I found that the reasoning models, while more expensive, generated better questions from my limited testing. Privacy: This GitHub Action runs a local webserver and uses ngrok to serve the quiz through a temporary url. Your code…
2025 · github.com · its alternatives →
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AI powered skill extraction tool
2022 · its alternatives →
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Convert your vector embeddings into a set of questions and their ideal responses. Use this dataset to test your LLM and catch failures caused by prompt or RAG updates. Get started in 3 lines of code: ``` pip3 install fiddlecube ``` ``` from fiddlecube import FiddleCube fc = FiddleCube(api_key="") dataset = fc.generate( [ "The cat did not want to be petted.", "The cat was not happy with the owner's behavior.", ], 10, ) dataset ``` Generate your API key: https://dashboard.fiddlecube.ai/api-key # Ideal QnA datasets for testing, eval and training LLMs Testing, evaluation or…
2024 · github.com · its alternatives →
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Simon(sfarshid) and I spend a lot of time on GitHub. As data nerds we put together a quick tool to explore your repository’s data. How it works: - Data Loading: We use dlt to pull data (issues, PRs, commits, stars) from GitHub - Semantic Layer: Relta wraps the underlying dataset into a semantic layer so the LLM doesn’t hallucinate. - Text-to-SQL: A text-to-SQL agent transforms your plain-English question into a query using the semantic layer - Generative Charts: assistant-ui dynamically generates a chart based on the SQL query - Refinements: If the semantic layer can’t handle your question,…
2024 · github.com · its alternatives →
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Extract boilerplate-free text from news links at scale
2021 · extractorapi.com · its alternatives →
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Turn any webpage into structured data using LLMs. Contribute to mishushakov/llm-scraper development by creating an account on GitHub.
2024 · github.com · its alternatives →
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We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
2023 · github.com · its alternatives →
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