#jupyter_notebook
Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
#mdx
This free interactive course teaches Product Managers to use Claude Code for daily tasks like processing notes, writing PRDs, analyzing data, and strategy planning through hands-on modules (4-6 hours). Clone the repo, run `claude`, and follow guided lessons with agents and file tools—no setup needed yet. You'll work faster, get instant multi-perspective feedback, and boost productivity without quality loss.
https://github.com/carlvellotti/claude-code-pm-course
#MDX result
1 and 2 target achieved in just 1 house 21 minutes ✅
One more huge quick profit 11%🤑💰🤑
👉 Still thinking? The more you wait more you lose profit
☎️ Contact @MichaelStrategiesVip for membership and grab next breakout signal
#MDX bounced back from the Trendline on 6H Time frame,we expect a good bullish momentum from the Green zone,send it to the moon 🚀
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️