Authored by Kalyan KS. To stay updated with LLM, RAG and Agent updates, you can follow me on LinkedIn, Twitter and YouTube.
Here is the RAG Beginner’s roadmap. This roadmap provides a structured learning path to mastering RAG from basics to deployment. 🚀
Python Programming Language
Python is the primary language for RAG development due to its rich AI ecosystem. It offers libraries like LangChain, LlamaIndex, and sentence-transformers for seamless implementation.
Generative AI Basics
Understanding how generative AI models work, including text generation, image generation, and multimodal AI, is crucial for building RAG applications.
LLM Basics
Large Language Models (LLMs) are trained on massive datasets to generate human-like text. RAG systems use LLMs to interpret the user query and generate the responses based on the retrieved context.
LLM Frameworks (LangChain or LlamaIndex)
These frameworks provides in-built functionalities to develop RAG applications.
Chunking
Chunking means splitting the document into small-size chunks so that relevant chunks can be given to the LLM. Different chunking strategies include fixed-size chunking, recursive chunking, agentic chunking, semantic chunking etc.
Data Extraction
Extracting structured data from unstructured documents (PDFs, HTML, text, etc.) is essential for building knowledge bases in RAG.
Embeddings
Embeddings convert text into high-dimensional numerical vectors, capturing semantic meaning. They are used for similarity search, retrieval, and document clustering in RAG systems.
Vector Databases
Vector databases like FAISS, ChromaDB, and Weaviate store and retrieve embeddings efficiently. They enable fast semantic search to find relevant chunks for LLMs.
RAG Basics
Retrieval-Augmented Generation (RAG) enhances LLMs by retrieving relevant knowledge before generation. This improves accuracy, reduces hallucinations, and enables real-time updates.
Implement RAG from Scratch
Building a RAG system from scratch involves designing retrieval, chunking, indexing, embedding storage, and querying mechanisms without relying on pre-built frameworks.
Implement RAG with LangChain or LlamaIndex
These frameworks simplify RAG implementation by providing built-in tools for document loading, embedding, retrieval, and LLM integration.
Agent Basics
Agents use reasoning, memory, and tool usage to interact with external systems and automate complex workflows. LLM-powered agents can dynamically retrieve and process data.
Agentic RAG
Agentic RAG combines retrieval-based knowledge with autonomous agent capabilities. It enables LLMs to make iterative queries, refine answers, and take actions based on retrieved information.
Advanced RAG Techniques
Advanced techniques include hybrid retrieval (semantic + keyword search), query rewriting, re-ranking etc.
Build RAG Apps
Building real-world RAG applications involves integrating UI, backend logic, and databases. Use Streamlit, FastAPI, or Flask to create interactive RAG-based systems.
RAG Evaluation & Monitoring
Evaluating RAG models requires metrics like retrieval precision, hallucination rate, and response relevance. Monitoring tools like LangSmith help analyze system performance.
Deploy RAG Apps
Deploying RAG applications involves hosting models, vector databases, and retrieval pipelines on cloud platforms like AWS, Azure, or Google Cloud for scalable access.