Context Engineering

Why Do We Need Context Engineering?

AI models have a limited working memory, known as the context window. If you give them too much irrelevant data or too little critical information, they fail.

If Prompt Engineering is about what you ask, Context Engineering is about everything the model knows while answering.

Prompt Engineering is like a chef writing a better recipe.
Context Engineering is like building the entire kitchen, stocking the pantry, and hiring a waiter who remembers exactly what the customer ordered last week.

What is Context Engineering?

Context Engineering is the deliberate design and control of all information provided to an AI model during inference.

Instructions – role, tone, goals
Background knowledge – documents, facts, constraints
Memory – conversation history, user preferences
Examples – few-shot demonstrations
Tools & rules – APIs, schemas, policies
Ordering & formatting of everything above

All of this together forms the context window the model reasons over.

Why Context Engineering Matters

1. Models Infer Intent from Context

LLMs do not truly “understand” intent. If context is unclear or noisy, the model:

Hallucinates
Overgeneralizes
Ignores constraints
Produces inconsistent results

2. Scales Beyond Simple Prompts

One clever prompt works for demos. Real systems need:

Multi-step workflows
Reliable agent behavior
Domain-specific expertise
Long-running conversations

3. Reduces Hallucinations

By grounding models with real documents (RAG), clear constraints, and task boundaries, AI stops guessing.

4. Specialization Without Retraining

Instead of fine-tuning models, you can inject:

Domain documents
Rules and schemas
High-quality examples

This is faster, cheaper, and more flexible.

Example

Poor Context

“Summarize this contract.”

Context-Engineered Version

You are a legal analyst.
Use only the provided contract text.
Identify obligations, termination clauses, and risks.
Output in bullet points.
Do not provide legal advice.

Same model. Very different outcome.

Context Engineering vs Prompt Engineering

Prompt Engineering

Focuses on wording
Usually one-shot
Mostly instructions
Manual

Focuses on system design
Persistent over time
Instructions + data + memory
Often automated

Key Benefits

Reduces hallucinations by grounding AI in real facts
Improves accuracy by removing irrelevant noise
Saves cost & time by using only high-value tokens
Provides memory by selectively bringing past context back

Models are only as good as the context you give them.
Context engineering is how you architect that context.

Quadratic Complex Problems in AI

Quadratic problems appear in AI when optimizing functions that include squared terms. These can be convex (easy to solve) or non-convex (complex), and understanding the difference helps in training AI models effectively.

Quadratic Problem

Convex Quadratic

Linear SVM Optimization
Quadratic Programming (Control)

Non-Convex Quadratic

Neural Network Loss Optimization
Reinforcement Learning Value Functions
Portfolio Optimization with Constraints

Solution Techniques

Gradient Descent & Variants
Simulated Annealing
Evolutionary Algorithms
Convex Relaxations