By Prady K | Published on DataGuy.in
1. Introduction: Two Eras, Same Objective — Optimize the Input
Machine learning and language models may live in different worlds, but they share a common truth: the quality of input often dictates the quality of output.
In the early years of machine learning, feature engineering was the make-or-break step. It involved extracting the right patterns from raw data—selecting, transforming, scaling—so that models could learn more effectively. Today, as we shift into the world of large language models (LLMs), a similar process is unfolding under a different name: context engineering.
Where ML relied on structured features, LLMs rely on structured context. And just as feature engineering shaped the ML era, context engineering is now shaping how we build intelligent, responsive, and reliable AI systems.
2. What is Feature Engineering? (ML Perspective)
Feature engineering refers to the practice of transforming raw data into features that make machine learning models perform better. It’s not about the model itself—it’s about feeding the model smarter signals.
This involves tasks like:
- Encoding categorical values
- Scaling numerical values
- Imputing missing data
- Generating domain-specific insights (e.g., time-based lags, frequency counts)
- Reducing dimensions to remove noise or redundancy
In many real-world projects, feature engineering has contributed more to performance gains than algorithm selection. It was the art of understanding the problem, and shaping the input to fit the task.
3. What is Context Engineering? (LLM Perspective)
Context engineering is the emerging discipline of shaping the inputs to large language models—beyond just prompt writing. It involves carefully constructing the full context window, which includes not just the immediate query but also memory, retrieved knowledge, tool outputs, and metadata about the task.
Why does it matter? Because the context window is what LLMs “see.” It’s the only lens through which they interpret the world and generate responses. The better we craft that lens, the better the reasoning.
Key techniques include:
- Context writing: framing tasks clearly, with goals, roles, and formats
- Context selection: pulling in relevant information from memory or external sources
- Compression: summarizing or trimming content to fit the token budget
- Isolation: partitioning context for multi-agent or tool-specific routing
4. Key Analogies: Feature vs. Context Engineering
| ML Feature Engineering | LLM Context Engineering |
|---|---|
| Feature selection | Context selection |
| Feature transformation | Context compression |
| Handling missing values | Filling context gaps |
| Feature encoding | Task + metadata formatting |
| Temporal features | Persistent memory / scratchpads |
| Dimensionality reduction | Token limit optimization |
5. Similar Roles in the Stack
Just as data scientists became experts in crafting features that make models more intelligent, prompt engineers and context designers now play a similar role in the LLM stack.
What’s important here is this: neither role is tied to a specific model. Feature engineering worked whether you were using XGBoost or random forests. Context engineering works whether you’re using GPT-4, Claude, Gemini, or Mistral.
Both act as model-agnostic performance multipliers. They allow teams to extract more value from the same underlying model by shaping better inputs.
6. Why Context Engineering Matters More in the Age of Foundation Models
Here’s the fundamental shift: most modern LLMs are closed weights—you can’t retrain or finetune them easily. That means:
Context is all you control.
In the age of foundation models, your influence comes not from training new parameters, but from curating the window of information passed into the model. This makes context engineering the most powerful tool we have to shape behavior, reasoning quality, and agent consistency.
And when you move into multi-agent systems—where tasks are long, feedback loops complex, and state must be preserved—context engineering evolves from helpful practice to necessary architecture.
Inspiration: This article was inspired by a thought-provoking LinkedIn post by Sudalai Rajkumar , which drew a sharp analogy between context engineering in LLMs and feature engineering in machine learning. His framing helped shape the foundational idea for this blog.
7. Conclusion: Context is the New Interface
If feature engineering was the scaffolding that helped machine learning models scale problems, context engineering is now the architecture that supports intelligent behavior in language-based systems.
We’re entering an era where tuning models takes a back seat—and designing inputs becomes the primary lever for innovation.
In short: prompt engineering was just the beginning. Context is the real frontier.
Explore the rest of our Context Engineering tag for deep dives into techniques, case studies, and evolving patterns in LLM system design.