Best Practices

Context Engineering Guide

Learn how to create better AI context based on research from Anthropic. Better context = better AI outcomes.

Based on Anthropic Research

This guide incorporates best practices from Anthropic's context engineering research for building effective AI agents. Read the original article

What is Context Engineering?

Context engineering is the art and science of providing AI with the right information, in the right format, at the right time. It's about optimizing everything that goes into the AI's context window to produce better outputs.

Good Context Engineering

• Specific, concrete details about identity and capabilities

• Clear examples that demonstrate expected outputs

• Structured information that's easy to process

• Explicit constraints on what to do and avoid

→ AI produces consistent, high-quality, relevant outputs

Poor Context Engineering

• Vague descriptions that could apply to anyone

• Abstract instructions without examples

• Unstructured, rambling information

• Missing constraints and boundaries

→ AI produces generic, inconsistent, off-target outputs

The Four Pillars of ContextFile.ai

ContextFile.ai provides four core components that map directly to context engineering best practices:

Context Files

System Prompt Design

Define WHO you are - identity, background, and capabilities

12 rich-text fields

Skills

Tool Architecture

Define HOW you work - workflows, processes, and methods

Instructions + Examples + Triggers

Tasks

Few-Shot Prompting

Define WHAT to do - prompts, variables, and outputs

Prompt templates + Variables

Use Cases

Sub-Agent Architecture

Orchestrate complete workflows with triggers and delivery

Context + Skill + Task + Integrations

Quality Principles

Four fundamental principles that improve any context you create:

Be Specific, Not Vague

AI performs better with concrete details than generic descriptions.

Good Example

Senior product manager with 8 years in B2B SaaS, specializing in AI-powered tools

Avoid

Experienced professional with various skills in technology

Show, Don't Tell

Examples are more effective than abstract instructions.

Good Example

Input: 'Review this function'\nOutput: [Complete code review with security focus]

Avoid

Write good code reviews that are thorough

Structure Matters

Well-organized context loads efficiently and processes better.

Good Example

**Role:** PM | **Focus:** AI Products | **Team:** 5 engineers

Avoid

I work as a PM and I focus on AI stuff and my team has some engineers

Include Constraints

Telling AI what NOT to do is as important as what to do.

Good Example

Never use jargon without explanation. Always cite sources.

Avoid

Write professionally and make it good

Context File Optimization

Not all fields are equally important. Focus on high-impact fields first:

Field	Priority	Weight	Why It Matters
Who Am I	Critical	15%	First thing AI reads - make it memorable
What We Do	Critical	15%	Concrete capabilities, not aspirations
Who We Serve	High	12%	Audience defines tone and complexity
Voice & Tone	High	12%	Directly shapes AI writing style
Company	Medium	10%	Organizational context and constraints
Goals	Medium	10%	What success looks like
Competition	Medium	8%	Positioning and differentiation
Sales Context	Medium	8%	Pricing, objections, personas
Numbers	Low	4%	Metrics for calibration
Team	Low	3%	Collaboration dynamics
Compliance	Low	2%	Regulatory constraints
Systems	Low	1%	Tech stack context

Pro Tip

The top 4 fields (Who Am I, What We Do, Who We Serve, Voice & Tone) account for 54% of your Context Quality Score. Focus improvements there first.

Skill Best Practices

Skills are tools for AI agents. Well-defined tools dramatically improve performance:

Detailed Instructions

Research shows detailed tool descriptions dramatically improve agent performance.

Tip: Use numbered steps, include what to do AND what to avoid, be explicit about format.

Few-Shot Examples

Examples are highly effective for guiding AI behavior and output format.

Tip: Show 2-3 diverse examples including edge cases. Show input AND expected output.

Specific Trigger Phrases

Auto-trigger phrases should be specific enough to avoid false positives.

Tip: Use 3-5 phrases, include variations, avoid generic phrases like 'help me'.

Clear Descriptions

AI uses descriptions to decide when to apply skills. Be specific about use cases.

Tip: Include keywords users might say. Describe WHEN to use, not just WHAT it does.

Task Prompt Structure

The order and structure of your prompt template directly impacts output quality:

System Prompt

Set AI's overall behavior and persona

Define role, expertise level, and constraints. Keep under 200 words.

Context

Provide background information

Place relevant context before instructions using {{contexts}} placeholder.

Instructions

Clear directive of what to do

Be specific and actionable. Include format requirements.

Examples

Show expected output format

Use outputExample field or inline examples in prompt.

Output Format

Specify how results should be structured

Match format to downstream use: JSON for integrations, Markdown for humans.

Example Prompt Structure

{{#if contexts}}
**Context:**
{{contexts}}
{{/if}}

{{#if skills}}
**Guidelines:**
{{skills}}
{{/if}}

---

**Task:**
{{variables.request}}

**Output Format:**
Provide your response as markdown with clear sections.

Use Case Orchestration

Use Cases combine all components into complete workflows. Think of them as orchestrating specialized sub-agents:

Context

WHO

Skill

HOW

Task

WHAT

Trigger

WHEN

Scheduled

Run daily, weekly, or monthly. Best for recurring tasks like reports.

Event-Based

Triggered by calendar events, webhooks, or external actions.

Manual

Run on-demand when you need results. Simple and flexible.

Quality Scoring

ContextFile.ai provides quality scores to help you improve your context:

Context Quality Score

Analyzes your context file based on:

Field completeness (20%)
Content specificity (25%)
Information structure (20%)
Field priority weighting (35%)

Use Case Health Score

Evaluates workflow completeness:

Component presence (Context, Skill, Task)
Trigger configuration
Output integrations
Execution performance

Context Engineering Tips

Start with high-impact fields first - they have the biggest effect on AI output quality.
Share your context file first (who you are), then apply skills as needed (how to do tasks).
Include examples in your skills - AI learns patterns better from demonstrations than instructions.
Use the AI Optimize feature to improve your instructions for clarity and effectiveness.
Test your context with different AI tools - good context works everywhere.

Context Engineering Guide

Based on Anthropic Research

What is Context Engineering?

The Four Pillars of ContextFile.ai

Quality Principles

Context File Optimization

Pro Tip

Skill Best Practices

Task Prompt Structure

System Prompt

Context

Instructions

Examples

Output Format

Example Prompt Structure

Use Case Orchestration

Quality Scoring

Context Engineering Tips

Related Documentation

Start Building Better Context

Context Engineering Guide

Based on Anthropic Research

What is Context Engineering?

The Four Pillars of ContextFile.ai

Quality Principles

Context File Optimization

Pro Tip

Skill Best Practices

Task Prompt Structure

System Prompt

Context

Instructions

Examples

Output Format

Example Prompt Structure

Use Case Orchestration

Quality Scoring

Context Engineering Tips

Related Documentation

Start Building Better Context