Context Engineering: The Six Pillars That Make Claude Code Reliable

Problem: Claude Code gives you inconsistent results. Sometimes brilliant, sometimes frustrating. You can't predict when it will nail your request or miss the mark entirely.

Quick Win: Stop stuffing everything into your prompt. Load context strategically:

# Bad: dump everything upfront
claude "Here's my entire codebase architecture, all conventions,
every pattern we use, plus the task..."
 
# Good: let Skills load what's needed, when needed
claude "Build the auth module"
# Skills load authentication patterns only when Claude needs them

The difference: prompt engineering asks questions well. Context engineering ensures Claude has the right information at the right time.

What Is Context Engineering?

Context engineering is the discipline of architecting how information flows to your AI model. It's what separates frustrating AI experiences from having a coding partner you can trust to bring any idea to life.

Think of it this way: a context window is finite workspace measured in tokens. Your instructions, retrieved documents, tool outputs, and conversation history all compete for space. When you hit limits, older information gets discarded. When you organize poorly, Claude loses track of what matters.

The insight: context is a scarce resource. How you structure it determines whether Claude delivers exactly what you envisioned or produces code that misses the point.

The Context Window Challenge

Claude Code fails in four predictable ways when context isn't managed well:

Recognize these patterns. They're the enemy.

The Six Pillars Framework

Context engineering has six interconnected components. Here's how each applies to Claude Code:

1. Agents

AI agents combine an LLM with tools, memory, and reasoning to achieve goals. They orchestrate context, deciding what surfaces, persists, or gets discarded.

Claude Code evolved from single-agent to multi-agent with the release of subagents. This matters for context engineering:

# Single agent: one context window handles everything
claude "Research, plan, build, test, and deploy the payment system"
 
# Multi-agent: specialized contexts, distributed load
# Central AI delegates to focused subagents
claude "Build the payment system"
# → Research agent gathers requirements
# → Backend agent builds Stripe integration
# → Frontend agent creates checkout UI
# → Each agent has clean, focused context

Multi-agent architectures prevent context confusion by scoping each agent's responsibility. Your central AI becomes the CTO coordinating specialists.

2. Query Augmentation

Real user queries are messy and unclear. Query augmentation refines them before execution.

When you position your Claude Code central AI as a co-founder or development manager, query augmentation happens naturally:

Your input: "fix the auth bug"

Central AI refinement:
→ Analyze recent changes to auth module
→ Identify error patterns in logs
→ Scope to affected files (src/lib/auth.ts)
→ Generate targeted fix with test coverage

Subagent receives: Clear, scoped task with context

The central AI funnels your rough ideas through its understanding before delegating. Subagents receive optimized prompts, not your raw input.

3. Retrieval

Retrieval pulls relevant information from external sources into the context window. This requires thoughtful chunking: small chunks give precise retrieval but miss surrounding context; large chunks provide rich context but consume more tokens.

Claude Code doesn't have native retrieval. Some workarounds exist through MCPs and CLI tools, but it's not currently a platform strength. For now, structure your CLAUDE.md and Skills to serve as your retrieval layer:

# CLAUDE.md - Your retrieval substitute
 
## Architecture (always loaded)
- Next.js 15, App Router, TypeScript strict
 
## Patterns (reference when needed)
See /docs/patterns/ for component conventions

4. Prompting Techniques

Here's what most people miss: stuffing information into the context window doesn't guarantee good performance. The method, order, and timing of how you layer information matters enormously.

Research shows the beginning and ending portions of the context window are more effective than the middle. This is why Skills work so well:

Conversation start:
├── CLAUDE.md (beginning of context - high attention)
├── Your initial prompt
├── ... conversation history ...
├── Claude's work
└── Skill loads HERE (end of context - high attention)
    └── Fresh, relevant instructions at peak attention

Before the skill loads, Claude operates lean and efficient. When the skill loads mid-conversation, it occupies the bottom of the context window, the high-attention zone, exactly when you need that expertise.

5. Memory

Memory transforms stateless models into systems that maintain context across interactions.

Claude Code's actual memory implementation:

With session management and evolving documentation, you create a memory layer highly specific to your repo. Claude both writes it (documenting decisions) and reads it (picking up where you left off). Over time, your Claude becomes proficient with your specific codebase.

6. Tools

Tools bridge reasoning and real-world action. Claude Code started with basics: Read, Write, Edit, Bash, and MCP for external integrations.

With Skills, Anthropic added something powerful: executable scripts that Claude can run without reading the code inside. This is the MCP-S CLI paradigm. The model follows a protocol without consuming context on implementation details.

Example: We built Context7 MCP into a documentation-research skill:

# .claude/skills/documentation-research/SKILL.md
---
name: documentation-research
description: Fetch library docs using Context7 API
---
 
## When to Use
User needs current documentation for any library
 
## Workflow
1. Resolve library ID via Context7
2. Fetch relevant documentation
3. Apply to current task
 
## Tools Available
- mcp__context7__resolve-library-id
- mcp__context7__get-library-docs

Claude invokes the MCP tools through a skill interface. Context-efficient, protocol-driven, no code reading required.

Implementing the Framework

Today: Audit your CLAUDE.md. Is it structured for retrieval? Are patterns documented where Claude can find them?

This week: Set up Skills for repeated workflows. Each skill prevents context confusion by loading expertise on-demand.

Ongoing: Watch for the four failure modes. When Claude starts repeating mistakes or ignoring context, you've hit contamination. Start fresh.

The Bottom Line

Reliable AI output isn't about bigger models. It's about engineering the flow of information to those models.

The six pillars work together:

• Agents distribute context across specialists
• Query augmentation refines messy input
• Retrieval (via CLAUDE.md/Skills) surfaces relevant info
• Prompting layers information strategically
• Memory maintains state across sessions
• Tools extend capabilities efficiently

Master these, and Claude Code becomes the coding partner you can trust with any feature or idea you have in mind.

Next steps:

• Context management for token optimization
• Memory optimization for persistence strategies
• Skills guide for on-demand expertise loading
• Sub-agent design for multi-agent architectures