The Rise of Context Engineering: A New Era for Retrieval-Augmented Generation

In the age of agentic AI, retrieval is evolving into a broader discipline that includes writing, compressing, isolating, and selecting context.

TL;DR
• RAG is evolving into context engineering, a broader discipline that includes retrieval, writing, compressing, isolating, and selecting context.
• Context engineering is a critical component of agentic AI, enabling agents to reason and make decisions.
• Knowledge graphs play a key role in the future of RAG, providing a metadata layer between relational and unstructured data.

Summary
Retrieval-Augmented Generation (RAG) has evolved significantly since its inception, transforming from static pipelines to governed, context-aware systems. Initially, RAG was used to retrieve relevant information to augment prompts sent to large language models (LLMs), improving their responses. However, its limitations, such as context poisoning and confusion, led to the development of new techniques. GraphRAG, a variation of RAG that uses knowledge graphs, has gained popularity, providing structure and meaning to enterprise data.

The rise of agentic AI has led to the evolution of RAG into context engineering, which involves dynamically writing, compressing, isolating, and selecting context across data and tools. This shift requires a semantic layer that standardizes data definitions and governance policies, enabling AI agents to understand, retrieve, and reason over various data types. Knowledge graphs will play a crucial role in this process, providing a metadata layer between relational and unstructured data.

Key predictions for the future of RAG include:
• RAG will continue to evolve into more agentic patterns, with retrieval becoming an iterative process.
• Retrieval will broaden to include all kinds of data, such as relational, content, images, audio, geodata, and video.
• Retrieval will include metadata about tools themselves, as well as 'memories.'
• Knowledge graphs will play a key role as a metadata layer.
• Evaluation metrics on context retrieval will gain popularity, focusing on accuracy, relevance, groundedness, provenance, coverage, and recency.

The future of RAG is not just about speed and accuracy but also about explainability and trust. As AI systems become more autonomous, the need for context engineering and semantic layers will grow, ensuring that agents can retrieve and reason over relevant data while respecting access control, policies, and regulations.

The future of RAG is not retrieval alone, but retrieval that's context-aware, policy-aware, and semantically grounded.