Prerequisites:

1). Basic Python OOP.

2). Familiarity with LLMs, Vector Database, Tokenization, and Embeddings.

3). Basic idea of how RAG frameworks work—retrieving context and feeding it to a generator model.

4). Web Scraping Basics.

Retrieval-Augmented Generation (RAG) has emerged as a transformative technique combining information retrieval with large language models (LLMs) to deliver accurate and context-aware AI responses. However, traditional RAG systems often face challenges including static retrieval strategies, outdated or irrelevant contextual data, and a lack of self-correction, which affect overall answer quality. This talk/article presents Self Adaptive Context RAG, an innovative system designed to dynamically tailor retrieval based on query complexity, scrape real-time web data for up-to-date information, and incorporate a dual-stage self-correction mechanism that critically evaluates and improves its own responses. Through intelligent web scraping, advanced text chunking, vector-based similarity search, and iterative reasoning, this system overcomes typical context limitations encountered by modern LLMs, providing more precise, trustworthy, and current answers. Attendees will gain insights into the architecture, implementation details, key challenges addressed, and practical usage of this cutting-edge RAG system—empowering developers and researchers to build smarter, more resilient AI-driven question-answering applications.