Context Engineering Methodology

Prompt engineering gets all the attention. But the real skill that separates effective AI usage from mediocre results is context engineering — the systematic practice of providing AI with the right information, constraints, and framing to produce useful outputs.

Why Prompts Aren’t Enough

A prompt is what you ask. Context is everything the AI needs to answer well.

Consider a developer asking AI to “write a function to process payments.” Without context, AI generates generic code. With context — your payment provider, error handling patterns, retry logic, logging standards, security requirements — AI generates code that actually fits your system.

Context engineering is the practice of systematically providing that surrounding information.

The Three Layers of Context

Layer 1: System Context

The foundational information that rarely changes:

• Technology stack and versions
• Coding standards and conventions
• Architecture patterns your team uses
• Security and compliance requirements
• Performance constraints

System context should be documented once and reused across all AI interactions. At WisdmLabs, we maintain context files that get prepended to AI prompts automatically.

Layer 2: Task Context

The specific information for the current task:

• Related files and their purposes
• Business requirements and acceptance criteria
• Known constraints and edge cases
• Previous decisions and their rationale

Task context changes with every request. The skill is knowing what’s relevant. Too little context produces generic output. Too much context produces confused output.

Layer 3: Feedback Context

The iterative information that improves results:

• What worked in previous attempts
• What failed and why
• Specific corrections and refinements
• Examples of desired output

Feedback context is where most developers give up too early. The first output from AI is rarely the best. The third or fourth iteration, with specific feedback, usually is.

Practical Patterns

Pattern 1: Context Files

Maintain a set of markdown files that describe your project’s conventions, architecture, and standards. Reference them in every AI interaction. Update them when decisions change.

Pattern 2: Progressive Disclosure

Start with minimal context. Add more only when AI output misses the mark. This helps you understand what context actually matters versus what’s noise.

Pattern 3: Example-Driven Context

Show AI examples of similar code in your codebase. “Write a service like UserService but for payments” is more effective than describing the pattern from scratch.

Pattern 4: Constraint-First Framing

Lead with what AI shouldn’t do. “Don’t use external libraries. Don’t add new database tables. Don’t modify the auth middleware.” Constraints are more actionable than open-ended instructions.

Measuring Context Quality

Good context produces outputs that:

• Require fewer iterations to reach acceptable quality
• Match your codebase’s conventions without correction
• Handle edge cases your team cares about
• Pass code review without style-related feedback

Track these metrics. They tell you whether your context engineering is improving.

Context engineering is the meta-skill of AI-assisted development. The developers who master it produce better results with any AI tool, regardless of the model or interface.