5 Days Roadmap to Learn RAG - Analytics Vidhya

Retrieval Augmented Generation (RAG): A 5-Day Learning Roadmap

RAG, short for Retrieval Augmented Generation, enhances Large Language Models (LLMs) by incorporating external knowledge sources. This addresses limitations of LLMs, such as hallucinations (fabricating information) and outdated knowledge. This hybrid approach combines the strengths of retrieval-based systems and LLMs for more accurate and relevant responses.

The RAG Process: A user query is sent to a retriever, which searches an external knowledge base. The retrieved documents are then fed to an LLM along with the original query, resulting in a more informed response.

Without RAG, LLMs face challenges like:

• Increased hallucination risk
• Outdated information
• Reduced accuracy and factual reliability

This 5-day roadmap provides a structured approach to learning RAG:

Day 1: RAG Fundamentals

• Understand RAG's purpose and importance in modern NLP.
• Learn the core components: retrieval and generation.
• Explore retrieval architectures (e.g., DPR, BM25) and generation architectures (e.g., GPT, BART, T5).

Day 2: Building a Retrieval System

• Deep dive into dense (DPR, ColBERT) and sparse (BM25, TF-IDF) retrieval.
• Implement basic retrieval using libraries like Elasticsearch or FAISS.
• Understand knowledge base structure and data preparation for retrieval.

Day 3: Fine-tuning a Generative Model

• Explore pre-trained models like T5, GPT-2, and BART.
• Fine-tune a model for tasks like question-answering or summarization.
• Understand how retrieval augments the generation process.

Day 4: Implementing a Working RAG System

• Combine retrieval and generation components.
• Utilize LlamaIndex's RAG pipeline for a practical implementation.
• Experiment with parameters like the number of retrieved documents and generation strategies.

Day 5: Building a Robust RAG System

• Advanced fine-tuning for domain-specific tasks.
• Scaling up with larger datasets and knowledge bases.
• Optimizing performance (memory, speed).
• Evaluating RAG models using metrics like BLEU and ROUGE.

This roadmap allows you to grasp the essentials of RAG within five days. For a hands-on approach, consider exploring a free course on building RAG systems using LlamaIndex.

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