LLM Technology

https://towardsdatascience.com/retrieval-augmented-generation-intuitively-and-exhaustively-explain-6a39d6fe6fc9

Retrieval Augmented Generation — Intuitively and Exhaustively Explain Making language models that can look stuff up

https://medium.aiplanet.com/advanced-rag-implementation-on-custom-data-using-hybrid-search-embed-caching-and-mistral-ai-ce78fdae4ef6

Advanced RAG Implementation on Custom Data Using Hybrid Search, Embed Caching And Mistral-AI What is RAG ?

https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1

Building RAG-based LLM Applications for Production (Part 1) In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on scale, evaluation and routing.

https://medium.com/codefile/analyse-your-data-with-the-openai-api-and-streamlit-and-nothing-else-2d38440a1650

Analyse your data with the OpenAI API and Streamlit — and Nothing Else Upload your CSV and ask the AI anything you like: summaries, insights, reports… and save the result — without using of extra libraries

https://medium.com/.baek.sd/creating-an-ai-web-service-using-langchain-with-streamlit-using-llama-2-or-chatgpt4-83a824e19435

Creating an AI web service using LangChain with Streamlit using LLaMA 2 or ChatGPT4 in your LOCAL… Create Chat with your PDF using Full stack in your local machine

https://python.plainenglish.io/chatbot-document-retrieval-asking-non-trivial-questions-c9b1560becb6

Chatbot Document Retrieval: Asking Non-Trivial Questions No more simple inquiries — let’s put the chatbots to the test with complex, data-driven questions.

https://cobusgreyling.medium.com/openai-gpt-3-5-turbo-model-fine-tuning-dd76f50db27f

OpenAI GPT-3.5 Turbo Model Fine-Tuning This article considers the process, speed and data requirements to create a fine-tuned model. On 22 August 2023 OpenAI announced the…

https://betterprogramming.pub/fine-tuning-your-embedding-model-to-maximize-relevance-retrieval-in-rag-pipeline-2ea3fa231149

Fine-Tuning Your Embedding Model to Maximize Relevance Retrieval in RAG Pipeline NVIDIA SEC 10-K filing analysis before and after fine-tuning embeddings

https://towardsdatascience.com/enhancing-csv-file-query-performance-in-chatgpt-3e2b67a5f867

Enhancing CSV File Query Performance in ChatGPT with LangChain’s Self-Querying based on a customized CSV Loader

https://towardsdatascience.com/10-ways-to-improve-the-performance-of-retrieval-augmented-generation-systems-5fa2cee7cd5c

10 Ways to Improve the Performance of Retrieval Augmented Generation Systems Tools to go from prototype to production

https://cobusgreyling.medium.com/self-critique-llm-chain-using-langchain-smartllmchain-d67c42a4fa83

Self-Critique LLM Chain Using LangChain & SmartLLMChain SmartLLMChain is a LangChain implementation of the self-critique chain principle. It is useful for particularly complex question answering…

https://python.langchain.com/docs/modules/data_connection/indexing?fbclid=IwAR1KOrouqRCQpAkpkXp61qSEuz5JZwXlixUgBL7IXGHEHDvFsnNc9GCZ1Kk

Indexing | 🦜️🔗 Langchain Here, we will look at a basic indexing workflow using the LangChain indexing API.