Shifting from prompt engineering to context engineering?

From Prompt Engineering to Context Engineering: The Next Evolution in Large Language Model Deployment

In the rapidly evolving landscape of AI and large language models (LLMs), a new paradigm shift is gaining momentum: moving beyond crafting perfect prompts to designing effective contextual frameworks. This transition, often referred to as “context engineering,” signifies a deeper focus on how we structure and manage information flow within AI systems.

Understanding the Shift

While the term “context engineering” gained notable attention following mentions by industry leaders like Andrej Karpathy, the underlying trend has been evident in real-world applications for some time. Companies deploying LLMs are increasingly discovering that the success or failure of their systems hinges less on prompt finesse and more on the quality and architecture of the context provided to the model.

Key Questions in Context-Centric Design

At large-scale deployment, critical considerations include:

• What information does the model genuinely require to produce accurate outputs?
• How should this information be organized to optimize comprehension?
• When should different pieces of context be introduced during a conversation or process?
• How do we maximize informational richness while adhering to token limitations?

Addressing these questions involves orchestrating retrieval mechanisms, managing memory buffers, integrating external tools, maintaining conversation histories, and ensuring safety—all within the constraints of the model’s context window.

Emerging Layers of Context

Experts are beginning to identify three distinct layers of contextual information:

1. Personal Context: Systems that adapt dynamically based on individual user behaviors. Examples include platforms like Mio.xyz, Personal.ai, and Rewind, which analyze emails, documents, and browsing data to deliver highly personalized AI interactions from the outset.

2. Organizational Context: Translating complex corporate knowledge into machine-readable formats. Tools such as Glean, Slack integrations, SAP, and specialized knowledge bases help bridge internal databases, discussions, and documentation, enabling smarter enterprise AI solutions.

3. External Context: Incorporating real-time data streams and external information sources. This could involve grounding LLM responses with live feeds or APIs from platforms like Exa, Tavily, Linkup, or Brave, making AI outputs more current and contextually relevant.

The Current Landscape

Despite these advancements, many AI projects still emphasize prompt optimization over robust context architecture. Common pitfalls include hallucinations—erroneous responses due to insufficient context—and escalating costs stemming from inefficient information handling.

Observations on Industry Trends

A recurring pattern emerges: organizations that prioritize designing comprehensive