openai-cookbook
Prompt-Engineering-Guide
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openai-cookbook
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Question-Answer System Architectures using LLMs
A pretrained LLM is a closed-book system: It can only access information that it was trained on. With domain fine-tuning, the system manifests additional material. An early prototype of this technique was shown in this OpenAi cookbook: For the target domain, text was embedded using an API, and then when using the LLM, embeddings were retrieved using semantic similarity search to formulate an answer. Although this approach evolved to retrieval-augmented generation, its still a technique to adapt a Gen2 (2020) or Gen3 (2022) LLM into a question-answering system.
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Ask HN: High quality Python scripts or small libraries to learn from
https://github.com/openai/openai-cookbook/blob/main/examples...
- Collection of notebooks showcasing some fun and effective ways of using Claude
- OpenAI Cookbook: Techniques to improve reliability
- OpenAI Cookbooks
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How to fine tune vit/convnet to focus on the layout of the input room image and ignore other things ?
It sounds like you are trying to tweak embeddings for similarity search. Rather than fine-tune the model's layers, you may want to try training a linear transformation the existing model's output embedding. Openai has a cookbook on how to do that. You will need some data though - but I think you can try it with ~20 pieces of synthetically generated data.
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Best base model 1B or 7B for full finetuning
tutorial from OpenAI https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb
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Resources to learn ChatGPT and the OpenAI API
OpenAI Cookbook
- OpenAI Cookbook
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Another Major Outage Across ChatGPT and API
OpenAI community repo with lots of examples: https://github.com/openai/openai-cookbook
Prompt-Engineering-Guide
- FLaNK AI - 15 April 2024
- Prompt Engineering Guide
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24 GitHub repos with 372M views that you can't miss out as a software engineer
Guides, papers, lecture, notebooks and resources for prompt engineering: https://github.com/dair-ai/Prompt-Engineering-Guide
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Resources to deepen LLMs understanding for software engineers
this has been a great resource. approachable and great for practitioners. it's frequently updated with new papers and techniques https://www.promptingguide.ai/
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Step-by-Step Guide to building an Anomaly Detector using a LLM
The idea behind prompt engineering is to construct the queries given to the language models to optimise their performance. This helps to guide them to generate the desired output by fine-tuning their response. There is a plethora of research papers out there on different forms of prompt engineering. DAIR.AI published a guide on prompt engineering that you might find useful to get started.
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The Essential Guide to Prompt Engineering for Creators and Innovators
Prompt Engineering Guide
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Getting Started with Prompt Engineering
Let's try to understand what is Prompt Engineering is all about. Here's the quote from Prompt Engineering Guide. DAIR-AI
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Microsoft/promptbase: All things prompt engineering
I found this resource [0] handy for getting a grasp on all the different terms people use (zero/one-shot, tree of thoughts, RAG, etc). It's not super detailed, but was enough for me (a professional developer) to get started on some side projects with Mistral.
[0] https://www.promptingguide.ai/
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OpenAI: Prompt Engineering
There are better guides out there too
- https://www.promptingguide.ai/readings
- https://github.com/dair-ai/Prompt-Engineering-Guide/tree/mai...
- https://github.com/microsoft/promptbase (this one is less of a guide, but is likely the current SoTA)
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Ask HN: Where to learn the cutting edge of prompt engineering?
- https://www.promptingguide.ai
What are some alternatives?
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
BetterChatGPT - An amazing UI for OpenAI's ChatGPT (Website + Windows + MacOS + Linux)
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
prompt-engineering - Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
askai - Command Line Interface for OpenAi ChatGPT
Learn_Prompting - Prompt Engineering, Generative AI, and LLM Guide by Learn Prompting | Join our discord for the largest Prompt Engineering learning community
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
awesome-chatgpt-prompts - This repo includes ChatGPT prompt curation to use ChatGPT better.
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
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