Learning never exhausts the mind
― Leonardo da Vinci
| Name | URL |
|---|
| LLM terminology | Link |
| A Critical Look at MCP | Link |
| Ilya Rice: How I Won the Enterprise RAG Challenge | Link |
| Paper | Link | Preview |
|---|
| A Comprehensive Overview of Large Language Models | Click | ref |
| KBLaM: Knowledge Base augmented Language Model | Click | |
| Retrieval-Augmented Generation for Large Language Models: A Survey | Click | ref |
| Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition | Click | |
| Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models | Click | |
| Google Prompt Engineering whitepaper | | ref |
| Speculative Thinking: Enhancing Small-Model Reasoning with Large Model Guidance at Inference Time | Click | |
| LLM Post-Training: A Deep Dive into Reasoning Large Language Models | | ref |
| Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder | Click | |
| What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? | Click | |
LLM can be used to map schema from one format to another. This is useful for data migration and integration.
| Resources | Link |
|---|
| blog (inspired by this blog) | Blog |
| paper (research paper on schema mapping) | ref |
LLM can be used to generate features for machine learning models. This can save time and effort in the feature engineering process.
| Resources | Link |
|---|
| paper (research paper on schema mapping) | ref |
| paper (research paper on schema mapping) | ref |
LLM can be used to convert PDFs into structured data. This is useful for extracting information from unstructured documents.