Summary
This study addresses how the approach used in interacting with AI models is evolving from "Prompt Engineering" to "Context Engineering." While Prompt Engineering focuses on optimizing the format and content of commands given to the model, Context Engineering involves the holistic structuring of contextual elements such as purpose, scope, target audience, style, constraints, and examples necessary for the model to produce output accurately and consistently. The research explains at a conceptual level how the context-based approach increases consistency, verifiability, and reproducibility, and proposes the KERNEL model as a methodological framework for context design.
Introduction
As AI model capabilities have increased, the forms of interaction with these models have also transformed. Initially, users focused on how to write the right commands, or prompts, to get efficient output from the model. However, the fact that today's models are trained on large, heterogeneous, and often ambiguous datasets has revealed that simply giving the right command is not sufficient.
This is where the concept of context comes to the forefront. Although context is generally translated as "bağlam" in Turkish, in AI interaction this concept refers not just to the framework of text or topic, but to a broad set of information encompassing purpose, target audience, style, boundaries, and example usage patterns. Therefore, context is a functional organizational domain that cannot be covered with a single-word translation.
Prompt Engineering and Its Limitations
Prompt Engineering aims to optimize the structure of commands to obtain a specific output from the model. However, this approach does not provide assurance about how accurate, consistent, or aligned with user expectations the output will be. This is because commands often don't specify why the task is being performed or under what conditions it would be considered correct.
For example:
- "Write a company introduction text." → The semantic goal and style are ambiguous.
- "Write an introduction text using corporate language." → There is direction, but the context is still limited.
Therefore, for output quality to improve, commands need to be supported with context.
Context Engineering: Conceptual Foundation
Context Engineering is the pre-structuring of the contextual foundation necessary for AI to accurately interpret the given task and produce output that is purpose-appropriate, verifiable, and consistent.
The fundamental basis of this approach is:
AI cannot independently infer under what conditions it has produced a correct result when completing a task. The model does not have the capacity to internally evaluate success criteria such as user satisfaction, style preferences, or accuracy measures. Therefore, explicitly including qualitative and quantitative conditions that will satisfy the user in the message request significantly increases output satisfaction. In other words, AI produces effective results when it knows not only what to do, but under what conditions it will be considered correctly done.
This approach provides:
- Consistency
- Reproducibility
- Evaluability
- Time and cost efficiency
in outputs.
The KERNEL Model for Context Design
The following KERNEL model for implementing Context Engineering offers a systematic framework:
K — Keep it simple
Explanation: The task should be clear; avoid unnecessary explanations. Example Application: "Create technical training material on Redis caching." Impact: Low cost and high focus
E — Easy to verify (Verifiable criteria)
Explanation: The success of the output should be measurable. Example Application: "Include 3 code examples." Impact: Output quality can be objectively evaluated
R — Reproducible results
Explanation: Avoid time-dependent expressions. Example Application: "Base it on Redis 6.0 features." Impact: Similar output production at different times
N — Narrow scope
Explanation: A prompt should contain only one goal. Example Application: Code writing + documentation not given in the same request. Impact: Task clarity increases
E — Explicit constraints
Explanation: Specifying what not to do is essential. Example Application: "Write in Python. No external libraries." Impact: Unwanted output rate decreases
L — Logical structure
Explanation: Prompt is structured in four-part format: Context → Task → Constraint → Output format Example Application: Standardization Impact: Scalable interaction model
Source: Reddit - Prompt Engineering
Vector Thinking and Conversation Refresh Requirement
AI models track interaction through linear information sequences (vector space). When the topic changes or the conversation becomes excessively long, the model may continue to carry traces of previous context. This situation can lead to false inferences known as hallucinations. Therefore, starting a new conversation when the topic changes allows the model to restructure its semantic space and produce more consistent outputs.
6. Conclusion
This study reveals at a theoretical level the necessity of context design in interacting with AI models. While Prompt Engineering focuses only on directing the model, Context Engineering increases output quality, consistency, and verifiability through structuring the task within a meaningful framework. Therefore, it is predicted that the primary focus of AI interaction design in the future will concentrate on context-centered, systematic, and reproducible message structures.