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. 🚀
https://github.com/milutinke/awesome-ai-llm-development
Last synced: about 12 hours ago
JSON representation
-
Section 1: The Basic Concepts
- What is AI, Machine Learning (ML), Deep Learning, Generative AI?
- Google's Introduction to Gen AI
- What are Large Language Models (LLMs)?
- What are tokens in LLMs?
- What are parameters in LLMs?
- Google's Introduction to Gen AI
- What are Large Language Models (LLMs)?
- What are tokens in LLMs?
- What are parameters in LLMs?
- What is a context window in LLMs? + How GPT works visualized in Excel
- What is temperature in LLMs?
- What are Top P and Top K in LLMs?
- How and where is Gen AI used?
- What is a context window in LLMs? + How GPT works visualized in Excel
- What is temperature in LLMs?
- What are Top P and Top K in LLMs?
- How and where is Gen AI used?
- Understanding Benchmarks
- Understanding Benchmarks
- What is AI, Machine Learning (ML), Deep Learning, Generative AI?
- What are Large Language Models (LLMs)?
- Google's Introduction to Gen AI
- What are tokens in LLMs?
- What are parameters in LLMs?
- What is a context window in LLMs? + How GPT works visualized in Excel
- What is temperature in LLMs?
- What are Top P and Top K in LLMs?
- How and where is Gen AI used?
- Understanding Benchmarks
-
Section 6: Advanced RAG Techniques
-
Actually Building Gen AI Apps Using OpenAI API Standard
- A Very Detailed Deep Dive into RAG + Various RAG Architectures by Stanford
- A Very Detailed Deep Dive into RAG + Various RAG Architectures by Stanford
- Building Production-Ready RAG Applications: Jerry Liu (2023, a bit outdated, but nice to know)
- Advanced RAG Optimization to Make It Production-Ready
- A Survey of Production RAG Pain Points and Solutions // Jerry Liu // AI in Production Conference (2024)
- Building Production RAG Over Complex Documents - by Databricks (2024)
- Advanced RAG Optimization to Make It Production-Ready
- A Survey of Production RAG Pain Points and Solutions // Jerry Liu // AI in Production Conference (2024)
- Building Production RAG Over Complex Documents - by Databricks (2024)
- Building Production-Ready RAG Applications: Jerry Liu (2023, a bit outdated, but nice to know)
- 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)
- Advanced RAG Optimization to Make It Production-Ready
-
-
Section 8: LLM AI Agents Frameworks
-
Agency Swarm
- Easy Deployment of AI Agencies
- Documentation
- Crash Course
- Sample Agents
- Creating an AI Agency with Cursor AI Editor in Plain English
- Documentation
- Crash Course
- Sample Agents
- Creating an AI Agency with Cursor AI Editor in Plain English
- Easy Deployment of AI Agencies
- Crash Course
- Sample Agents
- Creating an AI Agency with Cursor AI Editor in Plain English
- Easy Deployment of AI Agencies
-
Crew AI
-
LangGraph
-
AutoGen
-
-
Appendix
-
Production Pre-requisites
- Multi-threading Explained
- Multi-threading in Python
- Multi-threading Explained
- Multi-threading in Python
- Asynchronous Programming in Python - async/await
- Deep Dive into Asynchronous Programming with FastAPI
- WSGI & ASGI Simplified
- Uvicorn and FastAPI
- Production-Ready FastAPI Apps
- Logging
- Asynchronous Programming in Python - async/await
- Deep Dive into Asynchronous Programming with FastAPI
- WSGI & ASGI Simplified
- Uvicorn and FastAPI
- Production-Ready FastAPI Apps
- Multi-threading in Python
- Asynchronous Programming in Python - async/await
- Deep Dive into Asynchronous Programming with FastAPI
- WSGI & ASGI Simplified
- Uvicorn and FastAPI
- Production-Ready FastAPI Apps
- Logging
- WSGI & ASGI Simplified
-
Optional Stuff
- LLM Engineer's Handbook: From Theory to Production | TDE Workshop
- Large Language Models from Scratch (Simplified) - Part 1
- Large Language Models from Scratch (Simplified) - Part 2
- Attention Is All You Need
- Attention Is All You Need - Explained
- LLM Visualization Website
- LLM Engineer's Handbook: From Theory to Production | TDE Workshop
- Large Language Models from Scratch (Simplified) - Part 1
- Large Language Models from Scratch (Simplified) - Part 2
- Attention Is All You Need
- Attention Is All You Need - Explained
- LLM Visualization Website
- LLM Engineer's Handbook: From Theory to Production | TDE Workshop
- Large Language Models from Scratch (Simplified) - Part 1
- Large Language Models from Scratch (Simplified) - Part 2
- Attention Is All You Need - Explained
-
Miscellaneous
-
-
Section 2: Prompt Engineering - Basics
- Prompting Guide
- What is prompting?
- Prompting Guide
- What is prompting?
- Jeff Su's Prompt Formula
- Chain-of-Thought (CoT) Prompting
- Meta Prompting
- Use XML tags for prompt structuring
- Zero-Shot, First-Shot, Few-Shot Prompting
- Chain-of-Thought (CoT) Prompting
- Meta Prompting
- Use XML tags for prompt structuring
- What is prompting?
- Jeff Su's Prompt Formula
- Zero-Shot, First-Shot, Few-Shot Prompting
- Chain-of-Thought (CoT) Prompting
- Meta Prompting
-
Section 3: Building GenAI Apps with LLM APIs
-
Actually Building Gen AI Apps Using OpenAI API Standard
- Using OpenAI API - Documentation
- What are LLM Tools/Function Calls
- Using OpenAI API - Documentation
- Using ChatGPT/LLMs with OpenAI Python Package
- What are LLM Tools/Function Calls
- LLM Function/Tool Calling - Deep Dive + Examples
- OpenAI Assistants API
- What is LangChain?
- LangChain Master Class
- What is Langsmith?
- Langsmith Documentation
- Langsmith Crash Course (Video)
- LLM Function/Tool Calling - Deep Dive + Examples
- OpenAI Assistants API
- What is LangChain?
- LangChain Master Class
- What is Langsmith?
- Langsmith Documentation
- Langsmith Crash Course (Video)
- Using ChatGPT/LLMs with OpenAI Python Package
- What are LLM Tools/Function Calls
- LLM Function/Tool Calling - Deep Dive + Examples
- OpenAI Assistants API
- Langsmith Crash Course (Video)
- What is LangChain?
- LangChain Master Class
- What is Langsmith?
-
Section 4: Embeddings and Vector Databases
-
Actually Building Gen AI Apps Using OpenAI API Standard
- What are Embeddings?
- What are Embeddings (More Detailed)
- Word2Vec - word2vec/))* (*Optional for deeper understanding*)
- Vector Databases + Embeddings + Indexes
- Using Chroma DB + OpenAI Embedding
- What are Embeddings?
- What are Embeddings (More Detailed)
- Understanding How Vector Databases Work!
- A Long-Form Very Detailed Video + Retrieval Augmented Generation (RAG)
- Word2Vec - word2vec/))* (*Optional for deeper understanding*)
- Vector Databases + Embeddings + Indexes
- Using Chroma DB + OpenAI Embedding
- Using OpenAI Embeddings for CSV Search
- How to Choose a Vector Database?
- Using OpenAI Embeddings for CSV Search
- How to Choose a Vector Database?
- Understanding How Vector Databases Work!
- A Long-Form Very Detailed Video + Retrieval Augmented Generation (RAG)
- What are Embeddings?
- What are Embeddings (More Detailed)
- Vector Databases + Embeddings + Indexes
- Using Chroma DB + OpenAI Embedding
- Using OpenAI Embeddings for CSV Search
- How to Choose a Vector Database?
- Understanding How Vector Databases Work!
- A Long-Form Very Detailed Video + Retrieval Augmented Generation (RAG)
-
-
Section 5: Retrieval Augmented Generation (RAG)
-
Actually Building Gen AI Apps Using OpenAI API Standard
- What is RAG?
- RAG vs Fine-Tuning
- What is RAG?
- RAG vs Fine-Tuning
- Simple (Naive) RAG Implementation Using MongoDB
- Implementing RAG from Scratch + Different Techniques
- Simple (Naive) RAG Implementation Using MongoDB
- Implementing RAG from Scratch + Different Techniques
- What is RAG?
- RAG vs Fine-Tuning
- Simple (Naive) RAG Implementation Using MongoDB
- Different Text Chunking Techniques
- Evaluation Chunking Techniques on a specific Domain
- Implementing RAG from Scratch + Different Techniques
- Different Text Chunking Techniques
-
-
Section 7: LLM AI Agents
-
Actually Building Gen AI Apps Using OpenAI API Standard
-
For Programmers
-
-
Related
-
Section 7.1: LLM AI Agents (No-Code Tools)
Programming Languages
Categories
Section 3: Building GenAI Apps with LLM APIs
43
Appendix
41
Section 8: LLM AI Agents Frameworks
31
Section 1: The Basic Concepts
29
Section 4: Embeddings and Vector Databases
26
Section 2: Prompt Engineering - Basics
17
Section 6: Advanced RAG Techniques
15
Section 5: Retrieval Augmented Generation (RAG)
15
Section 7: LLM AI Agents
10
Related
8
Section 7.1: LLM AI Agents (No-Code Tools)
4
Sub Categories
Keywords
llms
6
awesome-llm
2
deepseek
2
flash-attention
2
flash-attention-2
2
flash-attention-3
2
llm
2
llm-inference
2
open-sora
2
paged-attention
2
sora
2
tensorrt-llm
2
vllm
2
python
2
rag
2
ai
2
awesome-list
2
deep-learning
2
fine-tuning
2
gpt
2
large-language-models
2
machine-learning
2
nlp
2
transformers
2