LLM for Physics Q&A

Which LLM should I use to generate high quality Q&A from physics textbook chapters? Question | Help I’m looking for LLMs to generate questions and answers from physics textbook chapters. The chapters I’ll provide can be up to 10 pages long and may include images. I’ve tried GPT, but the question quality is poor and often too similar to the examples I give. Claude didn’t work either as it rejects the input file, saying it’s too large. Which LLM model would you recommend me to try next? It doesn’t have to be free.It totally could be free though. All you would have to do is modify something like https://github.com/adamwlarson/ai-book-writer/tree/OpenTale so that you would use the story generation engine and then modify it to take in your PDFs or however you have the data saved and then use RAG to help with the creation of the material. You could probably make it very detailed as OpenTale is more than just concatenating things broken down by a table of contents with multiple calls. It has more entry points so you have to construct the context around it in order to really generate what you want. So you could supply the context from a larger dataset whether it be dataframes or just a folder full of PDFs.

Great question — generating high-quality **Q&A from physics textbook chapters** is a nuanced task, especially if the source is long and includes diagrams. Here's a breakdown of your best options — both **open-source** (potentially free) and **commercial** — tailored to your goal of detailed, high-quality educational Q&A generation. --- ## 🔬 Key Criteria for the Right LLM: - Handles **long context** (10+ pages of text) - Can reason about **scientific content** - Optionally supports **image input** (for diagrams/equations) - Allows **custom prompting** and RAG (Retrieval-Augmented Generation) - Not just summarization — must generate thoughtful **questions + answers** --- ## ✅ Recommended Models ### 1. **Gemma 7B or 2B (Google)** with RAG - **Why**: Extremely efficient open-source model with strong reasoning when paired with context windows via RAG. Perfect if you're modifying something like OpenTale. - **How**: Use it with LangChain + ChromaDB, reading from your chapter PDFs (OCR if needed). - **Pros**: Free, fast, fine-tunable, good open weights. - **Cons**: Needs setup, doesn’t handle images directly. - **Best for**: Local or cheap cloud-based batch processing. --- ### 2. **GPT-4 Turbo (OpenAI)** via API - **Why**: It can handle **300 pages** of context in one shot. Perfect for your 10-page chapters. - **How**: Use it with a good prompt template that: - Extracts key concepts - Writes different types of questions (MCQ, conceptual, derivations) - Answers with explanations - **Best Prompt Strategy**: - Split chapter into chunks - Pre-process concepts with an outline - Ask GPT to generate questions per concept - **Cons**: Cost (~$0.01 per 1K tokens in, $0.03 per 1K tokens out) --- ### 3. **Claude 3 Opus (Anthropic)** via API - **Why**: Strong at abstraction and educational content generation, more "teacher-like" than GPT. - **Note**: Claude's web app rejects large files, but the **API** supports longer contexts and better file handling. - **Best use**: If you have access to Claude API, retry with chunked chapters. --- ### 4. **Yi-34B, Mistral 7B, or Qwen2 (Open-source)** - Especially **Qwen2-72B** (if you can access it, e.g. via Together.ai, DeepInfra, or local GPU) - Combine with **RAG pipeline** - Use something like [`llama-index`](https://github.com/jerryjliu/llama_index) or [`LangChain`](https://www.langchain.com/) to: - Split the textbook into meaningful segments - Route questions into the LLM - Format the Q&A output --- ### 5. **LLaVA (Visual Language Model) + Text Model** - **Why**: If your chapters include images or diagrams you want interpreted - Combine LLaVA for **image captioning or description** → feed that into the **text LLM** - Example: “Image depicts force diagram of inclined plane...” → then generate questions from that --- ## 🧠 Workflow Suggestion (based on your OpenTale idea) 1. **Convert textbook chapters into structured JSON**: - Titles, subtopics, images, paragraphs 2. **Use RAG pipeline** (ChromaDB or FAISS) to allow targeted info retrieval 3. **LLM Generation** via prompt like: ```plaintext Based on the following subsection from a physics textbook, generate: - 2 Conceptual Questions - 1 Numerical Question - 1 Multiple Choice Question - Detailed answers for each Subsection: {text_chunk} ``` 4. **Optional**: Combine with **LangGraph** or **Smol Developer** to make the question generation multi-step and editable --- ## 🛠️ Tools to Modify or Extend | Tool/Library | Purpose | |-----------------------|--------------------------------| | `OpenTale` | Backbone story engine | | `llama-index` | Indexing + RAG for PDF chunks | | `LangChain` | Orchestration + chaining | | `unstructured` | Cleanly split textbook text | | `PyMuPDF / pdf2image` | Extract images from textbooks | | `LLaVA` | Convert diagrams to captions | --- ## 🧪 Pro-Tip: Prompt Engineering Matters Avoid simple prompts like “generate questions from this.” Instead: **Better Prompt:** ```plaintext You are a physics teacher preparing advanced review material. From the following text, generate: - A deep conceptual question - A multiple-choice question with 4 distractors - A worked example problem - Each answer should include a detailed explanation Text: {text} ``` --- ## Final Recommendations (Tiered) ### 💵 If you're okay paying: - **Use GPT-4 Turbo API** with LangChain RAG + smart prompts - For images, combine with **LLaVA** or use **Claude API** ### 💻 If you want free / local: - **Qwen2-72B or Yi-34B + LangChain RAG** - Optional: use **OpenTale backend** to inject structure - Add diagram reasoning via **LLaVA** --- If you want, I can help you write a working prototype with: - Your chosen LLM - The RAG setup - Integration into something like OpenTale or a Flask/React UI. Would you like that?

just explain in a few sentences the infra of the free set up

Sure — here's a quick explanation of the **free setup infrastructure**: 1. **Model**: Run a high-quality open-source LLM locally (e.g. **Qwen2-7B**, **Mistral 7B**, or **Yi-34B**) using **Ollama** or **LM Studio**. 2. **RAG Backend**: Use **LangChain** or **LlamaIndex** to: - Split textbook chapters (PDF/Markdown/HTML) into chunks - Store them in a local vector database (like **ChromaDB**) 3. **Pipeline**: When generating Q&A: - Query the vector DB to retrieve relevant chunks - Pass those + a smart prompt to the local LLM - Output questions and answers 4. **Optional Visual Input**: Use **LLaVA** locally if textbook diagrams are needed — it generates text from images which you feed into your pipeline. Everything runs locally and offline. No API keys or cloud cost.

Sure! A free setup for generating Q&A from physics chapters typically involves: 1. **Extracting and splitting** your textbook chapters (PDF or text) into smaller chunks using tools like `PyMuPDF` or `unstructured`. 2. **Indexing** these chunks with an open-source vector database like **ChromaDB** or **FAISS** for fast retrieval. 3. Using an open-source LLM (e.g., **Qwen2-7B**, **Mistral 7B**, or **LLaMA 2**) locally or via free cloud services. 4. Implementing a **Retrieval-Augmented Generation (RAG)** pipeline (with frameworks like **LangChain** or **llama-index**) to fetch relevant chunks from your index and feed them as context to the LLM. 5. Finally, prompting the LLM to generate high-quality questions and answers based on the retrieved content. This setup lets you leverage powerful models without paying for large API usage while handling longer chapters effectively.

in paragraph form

To set up a free system for generating high-quality questions and answers from physics textbook chapters, you start by extracting the content from your PDFs using tools like PyMuPDF or Unstructured, breaking the text into manageable chunks. These chunks are then indexed using a local vector database such as ChromaDB or FAISS, enabling fast semantic retrieval based on relevance. You pair this with a capable open-source language model like Qwen2-7B, Mistral 7B, or LLaMA 2, which you can run locally using frameworks like Ollama or LM Studio. A Retrieval-Augmented Generation (RAG) pipeline—typically built using LangChain or LlamaIndex—retrieves the most relevant content from your indexed textbook data and feeds it into the model along with a carefully crafted prompt that instructs the model to generate conceptual questions, multiple-choice items, or worked problems with answers. This infrastructure gives you a powerful and cost-free workflow for turning dense educational material into structured, pedagogically useful Q&A content.

generate a new stable diffusion prompt

Sure! Here's a new Stable Diffusion prompt based on a unique concept: **Prompt:** > *"A forgotten physics classroom buried in time, lit by flickering candlelight, with dusty chalkboards filled with ancient equations, broken globes, and overgrown vines creeping through shattered windows — hyper-detailed, cinematic lighting, 35mm film grain, surreal realism, moody atmosphere, by Zdzisław Beksiński and Alex Ross, concept art style, oil on canvas texture"* Let me know if you want to blend this with German Expressionism, glitch art, or any specific historical period or artist.