Here’s the 2025 LLM roadmap 1. Code and train your own LLM to really understand the fundamentals
2. Train models more conveniently using production-ready libraries
3. Learn about the big-picture considerations for real-world LLM/AI apps
CODE
-
2025 LLM Roadmap: From Fundamentals to Production Applications
By
–
-
Embedding Fine-Tuning Guide for RAG Systems
By
–
RAG Embedding Fine-Tuning Guide A practical guide demonstrating embedding model fine-tuning for RAG systems, featuring LangSmith monitoring and RAGAS metrics for performance evaluation. Check out the complete implementation https://
github.com/apatti/AIEBoot
camp/blob/main/09_Finetuning_Embeddings/Fine_tuning_Embedding_Models_for_RAG_using_RAGAS.ipynb
… -
PyTorch Democratizes Fast Kernel Writing Challenge
By
–
we've been working on democratizing fast kernel writing on the @PyTorch team. try the challenge, either you or your AI!
-
DeepSeek R1 Implementation with LangChain Azure Integration
By
–
DeepSeek R1 Tutorial Learn to implement DeepSeek's reasoning model using LangChain and Azure AI. This hands-on guide shows you model integration and streaming responses in action. Key features:
– LangChain-Azure integration
– Real-time streaming Watch: -
Databricks Fundamentals Badge Training Free One Hour
By
–
Have an hour to spare? Earn your Databricks Fundamentals badge today! In one hour, this free training will give you the basic skills needed to start leveraging AI throughout your data processes. Start training today: https://
dbricks.co/3D3Jeg3 -
Building IntelliJ AI Assistant with Deepseek and LangChain
By
–
IntelliJ AI Assistant Plugin Learn to build a coding assistant that integrates Deepseek AI into IntelliJ IDEA using LangChain. The plugin generates code and explains it in real-time, featuring Java Plugin DevKit and native IDE integration. Watch the tutorial now
-
Complete RAG Guide: LangChain Implementations and Advanced Retrieval
By
–
bRAG: Complete RAG Guide A comprehensive project showcasing RAG implementations with LangChain – from basics to advanced features like multi-query retrieval, ColBERT indexing, and RAG-Fusion. Check out this 1.7K+ starred guide https://
github.com/bRAGAI/bRAG-la
ngchain/
… -
AI & Machine Learning Model for House Price Prediction
By
–
3. AI & Machine Learning Model Development
— God of Prompt (@godofprompt) 23 février 2025
Prompt:
"Develop a machine learning model in Python that predicts house prices based on location, square footage, and amenities. Use Pandas, Scikit-Learn, and Matplotlib to train, test, and visualize the results." pic.twitter.com/bvtSalnioP3. AI & Machine Learning Model Development Prompt: "Develop a machine learning model in Python that predicts house prices based on location, square footage, and amenities. Use Pandas, Scikit-Learn, and Matplotlib to train, test, and visualize the results."
-
LangChain Korean Tutorial: RAG, Agents and Core Technologies
By
–
LangChain Korean Tutorial A comprehensive guide combining documentation, videos, and hands-on examples for mastering LangChain in Korean. Features practical implementations of RAG, agents, and core LangChain technologies. Check it out here https://
github.com/teddylee777/la
ngchain-kr
… -
Build AI Agents Fast with CopilotKit LangGraph
By
–
Build AI Agents Fast Create powerful AI agents with CopilotKit's CoAgents & LangGraph in just 30 minutes. Includes full stack implementation and LangSmith monitoring capabilities. Start building your agent now! https://
dev.to/copilotkit/age
nts-101-how-to-build-your-first-ai-agent-in-30-minutes-1042
…