Beyond RAG: Context Engineering for Smarter AI Systems

Author(s): Vikram Bhat

Originally published on Towards AI.

How Context Engineering Enhances Retrieval-Augmented Generation (RAG) for Smarter, More Reliable AI Applications.

Building a reliable chatbot on top of Large Language Models (LLMs) is far more than just plugging in a model and asking questions. Out of the box, LLMs often struggle with hallucinations, irrelevant answers, and context overflow when handling long or noisy documents. This is where Retrieval-Augmented Generation (RAG) shines — it grounds LLM responses in external sources by retrieving the most relevant content before generating answers.

The article explores how to enhance chatbot deployments using Retrieval-Augmented Generation (RAG) by introducing Context Engineering, a technique aimed at improving the response accuracy of chatbots powered by Large Language Models (LLMs). It discusses the architecture of a next-generation RAG pipeline that utilizes document ingestion, vector-based retrieval, and contextual compression to produce precise, trustworthy answers, thus elevating the chatbot’s performance and usability in real-world applications.

Read the full blog for free on Medium.

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Published via Towards AI