awesome-ai-llm-development
github.com/milutinke/awesome-ai-llm-development ↗A curated collection of practical resources for learning and building Generative AI applications with Large Language Models (LLMs). Perfect for beginners and experts alike, focusing on hands-on development over theory. 🚀
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Section 1: The Basic ConceptsSection 2: Prompt Engineering - BasicsSection 3: Building GenAI Apps with LLM APIsSection 4: Embeddings and Vector DatabasesSection 5: Retrieval Augmented Generation (RAG)Section 6: Advanced RAG TechniquesSection 7: LLM AI AgentsSection 7.1: LLM AI Agents (No-Code Tools)Section 8: LLM AI Agents FrameworksAppendixRelated
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me optional stuff resources from awesome-ai-llm-development"
Installation instructions →What's inside
Appendix
- 3Blue1Brown's Playlist for Neural Networks + LLMsOptional Stuff
- Asynchronous Programming in Python - async/awaitProduction Pre-requisites
async/await
- Attention Is All You NeedOptional Stuff
- Attention Is All You Need - ExplainedOptional Stuff
Explained
- Deep Dive into Asynchronous Programming with FastAPIProduction Pre-requisites
- How to choose an LLM based on price, speed and qualityMiscellaneous
Section 6: Advanced RAG Techniques
- Advanced RAG Optimization to Make It Production-Ready
- A Survey of Production RAG Pain Points and Solutions // Jerry Liu // AI in Production Conference (2024)
- A Very Detailed Deep Dive into RAG + Various RAG Architectures by Stanford
- Building Production RAG Over Complex Documents - by Databricks (2024)
by Databricks (2024)
- Building Production-Ready RAG Applications: Jerry Liu (2023, a bit outdated, but nice to know)
Section 7: LLM AI Agents
Section 7.1: LLM AI Agents (No-Code Tools)
- AI Foundations
- Making an AI agent in n8n using Gemini 2.0
- Nate Herk | AI Automation - Channel
AI Automation - Channel (An entire channel with a bunch of good tutorials and examples)
- RAG AI Agent with n8n + Supabase
Section 4: Embeddings and Vector Databases
Section 2: Prompt Engineering - Basics
Section 8: LLM AI Agents Frameworks
- Crash CourseLangGraph
- Crash CourseCrew AI
- Crash CourseAgency Swarm
- Crash CourseAutoGen
- Creating an AI Agency with Cursor AI Editor in Plain EnglishAgency Swarm
- CrewAI Flows Crash CourseCrew AI
Showing a sample of 93 resources. View the full list on GitHub →