The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices

🚀 Awesome System for Machine Learning ⚡️ AI System Papers and Industry Practice. ⚡️ System for Machine Learning, LLM (Large Language Model), GenAI (Generative AI). 🍻 OSDI, NSDI, SIGCOMM, SoCC, MLSys, etc. 🗃️ Llama3, Mistral, etc. 🧑‍💻 Video Tutorials.

Practice to LLM.

A curated list of awesome platforms, tools, practices and resources that helps run LLMs locally

LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.

Explore a comprehensive collection of resources, tutorials, papers, tools, and best practices for fine-tuning Large Language Models (LLMs). Perfect for ML practitioners and researchers!

A PyTorch coding practice platform — covering LLM, Diffusion, PEFT, and more A friendly environment to help you deeply understand deep learning components through hands-on practice. Like LeetCode, but for tensors. Self-hosted. Supports both Jupyter and Web interfaces.

Following emerging Large Language Model Operations (LLM Ops) best practices in the industry, you’ll learn all about the key technologies that enable Generative AI practitioners like you to leverage tools like LangChain, LLamaIndex, and more, to build complex LLM applications.

A linter with superpowers! 🔥 Use LLMs to enforce best practices across your codebase.

A comprehensive guide to LLM evaluation methods designed to assist in identifying the most suitable evaluation techniques for various use cases, promote the adoption of best practices in LLM assessment, and critically assess the effectiveness of these evaluation methods.

Explore a comprehensive collection of basic theories, applications, papers, and best practices about Large Language Models (LLMs) in genomes.

Python LeetCode practice environment with automated problem generation, data structure visualizations, and comprehensive testing. Includes all Grind 75, partial Blind, Neetcode and Algomaster problems with enhanced TreeNode/ListNode helpers, CI/CD pipeline, and LLM-assisted problem creation.

A curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and Large Language Models (LLMs).

This compendium reviews significant published research contributions and industrial engineering practices in leveraging Generative AI and LLMs for developing search, recommender, personalization, and question-answering systems. It aims to cover the entire spectrum of research and practices

Large Language Model Engineering (LLM Engineering) refers to the emerging best-practices and tools for pretraining, post-training, and optimizing LLMs prior to production deployment. Pre- and post-training techniques include unsupervised pretraining, supervised fine-tuning, alignment, model merging, distillation, quantization. and others.

Semgrep Pro Rules to ensure code using LLMs is following best practices

LLM Development, Design Patterns and Best Practices, First Edition - Published by Packt

Comprehensive tutorials for LangChain, LangGraph, and LangSmith using Groq LLM. Learn to build advanced AI systems, from basics to production-ready applications. Covers key concepts, real-world examples, and best practices. Ideal for beginners and experts alike. Elevate your AI development skills!

This is a tool that helps you quickly build backend services based on Asp.Net Core and EF Core. It provides command line, WebUI and IDE MCP support. In a well-designed project architecture that has been put into practice, code generation and LLM technology are used to reduce various template codes and greatly improve development efficiency!

llm_utils: Basic LLM tools, best practices, and minimal abstraction.

Terraform Skill for Claude Code and Codex. LLMs hallucinate a lot with Terraform - TerraShark fixes this. It eliminates hallucinations, is designed for modular and secure code and grounds your IaC in the official Hashicorp Terraform best practices.

[CVPR2025] Official implementation of the paper "Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices". (by Junyan Lin)

Claude Code best practices -- applied to application design. Interactive HLD/LLD visualization, implementation example. LLM-agnostic, DB-governed, GDPR-ready.

[NeurIPS 2024 D&B] Official code for "EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries"

Instructions for LLMs on how to produce great tests with best practices inside

Practices for improving quality and manageability of LLM co-created code-bases.

This is a repository of practicing the hands-on LLM from the book titled as "Hands-On Large Language Models" by Jay Alammar, Maarten Grootendorst

A targeted resource for mastering LangChain, featuring practice problems, code examples, and interview-focused concepts for building AI applications with Python. Covers chaining LLMs, memory management, and tool integration for technical interview success.

Fine-tuning an LLM using a Generic Workflow and Best Practices with PyTorch

A comprehensive, professional guide explaining the differences, strengths, and best practices of Retrieval-Augmented Generation (RAG) and Fine-Tuning for LLMs, including workflows, comparisons, decision frameworks, and real-world hybrid AI use cases.