How to Improve AI Performance/Responses Using Context

Large language models (LLMs), such as those powering ChatGPT, are not only intelligent—they become even smarter through precise management of the information they receive. This is exactly what a new review paper by Lingrui Mei, Jiayu Yao, Yuyao Ge, and others discusses, published on arXiv under the number 2507.13334v2. This comprehensive document, which analyzes more than 1,400 research papers, presents context engineering as a formal discipline that goes far beyond simple prompt design. Instead, it focuses on the systematic optimization of information packages for LLMs, improving their performance during inference. Let’s look at the most interesting parts of this review—from basic components to complex systems—and why it could change the future of AI.

The Fundamental Pillars of Context Engineering

The authors present a comprehensive taxonomy that divides context engineering into three basic components: context retrieval and generation, context processing, and context management. Each plays a key role in how LLMs process information.

Let’s begin with context retrieval and generation. This area deals with how LLMs obtain relevant data from external sources or create it themselves. For example, retrieval-augmented generation (RAG)—a technique in which a model retrieves information from databases and then integrates it into its responses—is described here in detail. The authors cite work by Patrick Lewis and others, who demonstrated in 2020 how RAG improves knowledge-intensive NLP tasks. Context generation then includes methods in which the model itself creates additional information, for example through chain-of-thought prompting, as proposed by Takeshi Kojima and colleagues in 2022. This allows LLMs to "think aloud" and solve complex problems step by step.

Another component is context processing, which focuses on optimizing information for efficient use. Here, the authors emphasize techniques such as context compression to reduce input length without losing key details. For example, a 2023 paper by Woosuk Kwon and his team describes efficient memory management for serving LLMs using PagedAttention, making it possible to process longer contexts without an enormous increase in computational requirements. Context management then addresses how to maintain the consistency and relevance of information throughout interactions, including mechanisms such as selective gating, inspired by work by W. Kruijne and colleagues from 2019.

These components are not isolated—the authors show how they intertwine to create a robust foundation for advanced AI systems. It is fascinating how the review emphasizes that the right context can dramatically improve LLM accuracy, for example in tasks such as question answering or code generation.

Advanced Systems: From RAG to Multi-Agent Architectures

The most interesting part of the review comes when the authors describe how these components are integrated into sophisticated systems. The first is retrieval-augmented generation (RAG), which combines retrieval with generation. According to the review, RAG gives LLMs access to up-to-date data beyond their training set, addressing the problem of hallucinations—when the model invents facts. For example, they cite a 2023 paper by Yunfan Shao and his team, who demonstrated how synthetic prompting improves chain-of-thought demonstrations.

Another category is memory systems, which enable LLMs to maintain long-term memory. The authors refer to work such as Mem1 by Aarush Sinha and colleagues from 2025, which teaches synergy between memory and reasoning for efficient long-horizon agents. This is crucial for applications such as conversational agents, where the model must remember previous interactions.

Next comes tool-integrated reasoning, in which LLMs work with external tools such as calculators or databases. The review cites Toolformer by Timo Schick and his team from 2023, which shows how models can teach themselves to use tools. Finally, there are multi-agent systems, where multiple agents collaborate, as in a 2023 paper by Guohao Li and his team on communicative agents for exploring LLM society.

These systems show how context engineering transforms LLMs from passive generators into active, adaptive entities. The review analyzes more than 1,400 citations, underscoring the depth of the research.

Tips for Effective Prompt and Context Design

If you want to get excellent responses from LLMs, the authors of the review emphasize that traditional prompt engineering—that is, manually or automatically searching for the ideal static sequence of words—is only the beginning. Instead, they recommend moving toward dynamic context engineering, where context is not fixed but assembled from multiple components through functions such as Retrieve or Select. For example, in the table on page 10 of the review, they compare how prompt engineering maximizes the probability Pθ(Y | prompt), while context engineering optimizes the entire system F for expected reward across tasks τ. Tip number one: Maximize relevant information in the context within the length limit Lmax to prevent the loss of detail—this means using techniques such as compression or selection, inspired by work such as FlashAttention by Tri Dao from 2022, which makes attention more efficient for long sequences. Another tip: Make systems stateful, not stateless—maintain memory (c_mem) and state (c_state), which increases scalability and reduces fragility in complex tasks. The authors warn against overly long prompts, which increase error rates, and propose system-level optimization in which the context adapts to the task, for example during multi-step reasoning. In practice: Start with chain-of-thought, add retrieval for facts, and integrate tools for calculations—this leads to more accurate, creative outputs without hallucinations. According to the review, this approach addresses the asymmetry between understanding and generation, which is key to future advances.

Research Gaps and the Future

The review reveals a key problem: a fundamental asymmetry between model capabilities. While current LLMs, enhanced by context engineering, excel at understanding complex contexts, they have significant limitations in generating equally sophisticated, lengthy outputs. The authors emphasize that closing this gap is a priority for future research.

They also address ethical issues such as data privacy in memory systems and call for a unified framework for both researchers and engineers. With 166 pages and 1,411 citations, this review is a technical roadmap showing the path toward more advanced, context-aware AI.

In conclusion, context engineering is not just a buzzword—it is a path toward more effective AI. If you are interested in how LLMs can become even better, this review by Lingrui Mei and the team is essential reading.