I Built RAG From Scratch in Python to Understand It. Here's What I Learned.

1. 1Build a RAG pipeline using pypdf, ChromaDB, and Ollama
2. 2Design and implement chunking algorithms for efficient text processing
3. 3Apply prompt design principles to improve model performance
4. 4Test and evaluate the RAG pipeline using various metrics
5. 5Configure and fine-tune the model to achieve optimal results

AI engineers and researchers on a team can benefit from understanding the inner workings of RAG pipelines, allowing them to fine-tune and optimize their models more effectively. This knowledge can also be applied to improve the performance of language models in various applications

💡 Building a RAG pipeline from scratch can provide a deeper understanding of the underlying mechanisms and improve model performance

💡 Building a RAG pipeline from scratch reveals the power of embeddings, chunking, and prompt design #AI #LLMs