​Beyond Prompting: The Emerging Discipline of Context Engineering

​The performance of Large Language Models isn’t just about the model itself — it’s about the context you give it.

​This paper:

​A Survey of Context Engineering for Large Language Models

​introduces “Context Engineering” as a formal discipline, going far beyond prompt design to the systematic optimization of the information payloads we feed into LLMs.

​Covering over 1,400 research papers, the authors present a taxonomy of context engineering, breaking it down into:

• ​Foundational components: context retrieval, generation, processing, and management

• ​System implementations: retrieval-augmented generation (RAG), memory systems, tool-integrated reasoning, and multi-agent systems

​The survey also exposes a key gap: while LLMs are increasingly adept at understanding complex contexts, they still struggle to generate equally sophisticated long-form outputs. Closing this gap is a major challenge for the future of context-aware AI.

​What we’ll cover:

• ​The full taxonomy of context engineering

• ​How RAG, memory systems, and multi-agent setups operationalize context

• ​Research trends and where the biggest gaps remain

• ​How practitioners can apply these insights in production systems

​📅 Date: Thursday, September 4th

​🕚 Time: 11 AM ET

​💡 Special note: This is the second feature in our first-ever double feature month! The first paper, Context Rot: How Increasing Input Tokens Impacts LLM Performance, is happening on August 28th. Join that session too →

​Join the #reading-group channel in the MLOps Community Slack to connect before and after the session.