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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: 4 days ago
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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
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Section 6: Advanced RAG Techniques
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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- Building Production-Ready RAG Applications: Jerry Liu (2023, a bit outdated, but nice to know)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)
- Advanced RAG Optimization to Make It Production-Ready
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Section 8: LLM AI Agents Frameworks
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Agency Swarm
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Crew AI
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LangGraph
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AutoGen
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Appendix
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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
- WSGI & ASGI Simplified
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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
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Miscellaneous
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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
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Section 3: Building GenAI Apps with LLM APIs
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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)
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Section 4: Embeddings and Vector Databases
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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)
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Section 5: Retrieval Augmented Generation (RAG)
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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
- Different Text Chunking Techniques
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Section 7: LLM AI Agents
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Actually Building Gen AI Apps Using OpenAI API Standard
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Related
Programming Languages
Categories
Section 3: Building GenAI Apps with LLM APIs
30
Appendix
30
Section 8: LLM AI Agents Frameworks
23
Section 1: The Basic Concepts
19
Section 4: Embeddings and Vector Databases
18
Section 2: Prompt Engineering - Basics
12
Section 6: Advanced RAG Techniques
10
Section 5: Retrieval Augmented Generation (RAG)
9
Related
8
Section 7: LLM AI Agents
6
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