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layout default
title Chapter 4: Advanced Patterns
parent Fabric Tutorial
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Chapter 4: Advanced Patterns

Welcome to Chapter 4: Advanced Patterns. In this part of Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

Master sophisticated pattern techniques for complex cognitive tasks and specialized domains.

Overview

Advanced patterns go beyond simple text processing to handle complex multi-step tasks, domain-specific analysis, and nuanced outputs. This chapter explores sophisticated pattern usage and customization.

Multi-Stage Patterns

Sequential Processing

# Pattern: deep_analysis
# IDENTITY and PURPOSE

You are a master analyst who performs comprehensive multi-stage analysis.
You process information through multiple analytical lenses.

# STAGES

## Stage 1: Surface Analysis
- Identify main topic and themes
- Note key entities and relationships
- Summarize basic structure

## Stage 2: Deep Analysis
- Uncover underlying assumptions
- Identify logical structure
- Find implicit claims

## Stage 3: Critical Analysis
- Evaluate evidence quality
- Identify potential biases
- Assess logical validity

## Stage 4: Synthesis
- Integrate findings from all stages
- Generate novel insights
- Formulate recommendations

# OUTPUT FORMAT

## SURFACE ANALYSIS
[Stage 1 findings]

## DEEP ANALYSIS
[Stage 2 findings]

## CRITICAL ANALYSIS
[Stage 3 findings]

## SYNTHESIS
[Integrated insights and recommendations]

# INPUT

{{input}}

Iterative Refinement

# Multi-pass processing
cat document.txt | fabric -p extract_claims > claims.txt
cat claims.txt | fabric -p verify_claims > verified.txt
cat verified.txt | fabric -p synthesize_findings > final.txt

# Or as a pipeline
cat document.txt | \
    fabric -p extract_claims | \
    fabric -p assess_evidence | \
    fabric -p generate_conclusions

Domain-Specific Patterns

Academic Research

# Pattern: analyze_paper
# IDENTITY and PURPOSE

You are a senior academic researcher and peer reviewer with expertise
in critically evaluating scholarly papers across multiple disciplines.

# ANALYSIS FRAMEWORK

1. **Methodology Assessment**
   - Study design appropriateness
   - Sample size and selection
   - Statistical methods used
   - Potential confounds

2. **Evidence Evaluation**
   - Data quality and completeness
   - Results interpretation
   - Effect sizes and significance
   - Replication considerations

3. **Contribution Assessment**
   - Novelty of findings
   - Theoretical implications
   - Practical applications
   - Future research directions

# OUTPUT FORMAT

## PAPER SUMMARY
Brief overview of the paper's purpose and findings.

## METHODOLOGY CRITIQUE
[Assessment of methods]

## EVIDENCE QUALITY
[Evaluation of data and results]

## CONTRIBUTION
[Assessment of paper's contribution]

## RECOMMENDATIONS
[Suggestions for authors/readers]

## RATING
[Overall quality score: A/B/C/D/F with justification]

# INPUT

{{input}}

Technical Documentation

# Pattern: analyze_architecture
# IDENTITY and PURPOSE

You are a senior software architect with 20+ years of experience
designing and reviewing complex systems.

# ANALYSIS AREAS

1. **Design Patterns**
   - Identify patterns used
   - Assess pattern appropriateness
   - Note anti-patterns

2. **Scalability**
   - Horizontal/vertical scaling capability
   - Bottleneck identification
   - Load handling assessment

3. **Security**
   - Authentication/authorization design
   - Data protection measures
   - Attack surface analysis

4. **Maintainability**
   - Code organization
   - Documentation quality
   - Technical debt indicators

# OUTPUT FORMAT

## ARCHITECTURE OVERVIEW
[Summary of system architecture]

## DESIGN ANALYSIS
[Pattern identification and assessment]

## SCALABILITY ASSESSMENT
[Scaling capability analysis]

## SECURITY REVIEW
[Security posture evaluation]

## RECOMMENDATIONS
[Improvement suggestions prioritized by impact]

# INPUT

{{input}}

Business Analysis

# Pattern: competitive_analysis
# IDENTITY and PURPOSE

You are a strategic business analyst specializing in competitive
intelligence and market positioning.

# ANALYSIS FRAMEWORK

1. **Product Analysis**
   - Feature comparison
   - Value proposition
   - Differentiation factors

2. **Market Position**
   - Target segments
   - Market share indicators
   - Brand positioning

3. **Strengths & Weaknesses**
   - Core competencies
   - Competitive advantages
   - Vulnerability areas

4. **Strategic Implications**
   - Opportunities
   - Threats
   - Recommended responses

# OUTPUT

## EXECUTIVE SUMMARY
[One paragraph overview]

## PRODUCT COMPARISON
| Feature | Target | Competitor A | Competitor B |
|---------|--------|--------------|--------------|

## SWOT ANALYSIS
### Strengths
### Weaknesses
### Opportunities
### Threats

## STRATEGIC RECOMMENDATIONS
[Prioritized action items]

# INPUT

{{input}}

Context-Aware Patterns

With System Context

# Provide additional context
fabric -p analyze_code \
    --context "This is a Python web application using FastAPI" \
    < main.py

# Multi-context
fabric -p review_design \
    --context "Industry: Healthcare" \
    --context "Compliance: HIPAA required" \
    < architecture.md

Role Customization

# Pattern: expert_review
# IDENTITY and PURPOSE

You are a {{expertise}} expert reviewing {{content_type}}.

Your specific expertise includes:
- {{specialty_1}}
- {{specialty_2}}
- {{specialty_3}}

# REVIEW CRITERIA

Apply standards appropriate for {{industry}} context.
Consider {{compliance_requirements}} if applicable.

# INPUT

{{input}}

Usage:

fabric -p expert_review \
    --var expertise="cybersecurity" \
    --var content_type="network architecture" \
    --var specialty_1="penetration testing" \
    --var specialty_2="compliance auditing" \
    --var specialty_3="incident response" \
    --var industry="financial services" \
    --var compliance_requirements="PCI-DSS, SOX" \
    < network_design.md

Output Formatting

Structured JSON Output

# Pattern: extract_structured
# IDENTITY and PURPOSE

You extract structured data from unstructured content.

# OUTPUT FORMAT

Output valid JSON only, no additional text:

```json
{
  "title": "string",
  "summary": "string",
  "key_points": ["string"],
  "entities": {
    "people": ["string"],
    "organizations": ["string"],
    "locations": ["string"]
  },
  "sentiment": "positive|negative|neutral",
  "topics": ["string"],
  "confidence": 0.0-1.0
}

INPUT

{{input}}


### Markdown Tables

```markdown
# Pattern: compare_items
# IDENTITY and PURPOSE

You create comprehensive comparison analyses in table format.

# OUTPUT FORMAT

## Comparison Summary
[Brief overview]

## Feature Comparison

| Feature | Item A | Item B | Winner |
|---------|--------|--------|--------|
| Feature 1 | Value | Value | A/B/Tie |
| Feature 2 | Value | Value | A/B/Tie |

## Detailed Analysis

### Item A
[Strengths and weaknesses]

### Item B
[Strengths and weaknesses]

## Recommendation
[Which to choose and why]

# INPUT

{{input}}

Pattern Parameters

Temperature Control

# Low temperature for factual extraction
fabric -p extract_facts --temperature 0.1 < document.txt

# Medium temperature for analysis
fabric -p analyze_trends --temperature 0.5 < data.txt

# Higher temperature for creative tasks
fabric -p brainstorm_ideas --temperature 0.8 < brief.txt

Token Limits

# Short output
fabric -p summarize --max-tokens 500 < long_document.txt

# Detailed output
fabric -p deep_analysis --max-tokens 4000 < document.txt

Response Format

# Force JSON response
fabric -p extract_data --response-format json < document.txt

# Stream output
fabric -p long_analysis --stream < large_document.txt

Pattern Composition

Pattern Chains

# Create a named chain
alias research_chain='
    fabric -p extract_claims | \
    fabric -p verify_facts | \
    fabric -p synthesize
'

# Use the chain
cat article.txt | research_chain > analysis.md

Conditional Processing

# Route based on content type
process_content() {
    local content=$(cat)
    local type=$(echo "$content" | fabric -p classify_content --json | jq -r '.type')

    case $type in
        "code")
            echo "$content" | fabric -p explain_code
            ;;
        "article")
            echo "$content" | fabric -p summarize
            ;;
        "data")
            echo "$content" | fabric -p analyze_data
            ;;
        *)
            echo "$content" | fabric -p general_analysis
            ;;
    esac
}

cat input.txt | process_content

Aggregation Patterns

# Process parts and combine
process_sections() {
    local input=$1

    # Split into sections
    csplit -f section_ "$input" '/^##/' '{*}'

    # Process each section
    for section in section_*; do
        fabric -p analyze_section < "$section"
    done | fabric -p combine_analyses
}

Summary

In this chapter, you've learned:

  • Multi-Stage Patterns: Complex analytical workflows
  • Domain-Specific: Specialized patterns for different fields
  • Context-Aware: Adding context and role customization
  • Output Formatting: Structured JSON and markdown tables
  • Pattern Parameters: Temperature, tokens, and format control
  • Pattern Composition: Chaining and conditional processing

Key Takeaways

  1. Layer Complexity: Build sophisticated analysis through stages
  2. Domain Expertise: Encode domain knowledge in patterns
  3. Context Matters: Provide relevant context for better results
  4. Structure Output: Define clear output formats for consistency
  5. Compose Patterns: Combine patterns for complex workflows

Next Steps

Ready to learn about composing complex workflows with Stitches? Let's explore Chapter 5.

Ready for Chapter 5? Stitch Composition

Generated for Awesome Code Docs

What Problem Does This Solve?

Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for fabric, input, Pattern so behavior stays predictable as complexity grows.

In practical terms, this chapter helps you avoid three common failures:

  • coupling core logic too tightly to one implementation path
  • missing the handoff boundaries between setup, execution, and validation
  • shipping changes without clear rollback or observability strategy

After working through this chapter, you should be able to reason about Chapter 4: Advanced Patterns as an operating subsystem inside Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI, with explicit contracts for inputs, state transitions, and outputs.

Use the implementation notes around ANALYSIS, content, IDENTITY as your checklist when adapting these patterns to your own repository.

How it Works Under the Hood

Under the hood, Chapter 4: Advanced Patterns usually follows a repeatable control path:

  1. Context bootstrap: initialize runtime config and prerequisites for fabric.
  2. Input normalization: shape incoming data so input receives stable contracts.
  3. Core execution: run the main logic branch and propagate intermediate state through Pattern.
  4. Policy and safety checks: enforce limits, auth scopes, and failure boundaries.
  5. Output composition: return canonical result payloads for downstream consumers.
  6. Operational telemetry: emit logs/metrics needed for debugging and performance tuning.

When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.

Source Walkthrough

Use the following upstream sources to verify implementation details while reading this chapter:

  • GitHub Repository Why it matters: authoritative reference on GitHub Repository (github.com).
  • Pattern Library Why it matters: authoritative reference on Pattern Library (github.com).
  • Community Patterns Why it matters: authoritative reference on Community Patterns (github.com).
  • AI Codebase Knowledge Builder Why it matters: authoritative reference on AI Codebase Knowledge Builder (github.com).

Suggested trace strategy:

  • search upstream code for fabric and input to map concrete implementation paths
  • compare docs claims against actual runtime/config code before reusing patterns in production

Chapter Connections