Best Practices

Prompt Engineering

Effective prompt engineering is crucial for reliable AI agent performance. Follow these proven patterns for consistent results.

Prompt Structure Best Practices

✅ DO: Clear Role Definition


You are a customer support specialist for a SaaS company.
Your role is to help customers with account issues, 
billing questions, and technical troubleshooting.

Always maintain a helpful, professional tone and
provide actionable solutions when possible.

❌ DON'T: Vague Instructions


Help customers with their problems.
Be nice and solve issues.

Prompt Engineering Do's and Don'ts

✅ DO

Provide specific examples of desired output
Define clear boundaries and limitations
Include context about the business domain
Specify output format requirements
Include error handling instructions
Test prompts with edge cases

❌ DON'T

Use ambiguous or contradictory instructions
Overload prompts with too much information
Assume the AI understands implicit context
Use inconsistent terminology
Skip validation of prompt outputs
Deploy without testing edge cases

Prompt Testing Checklist

Test with typical inputs and expected edge cases
Verify output format consistency across multiple runs
Check behavior with missing or incomplete input data
Validate response quality with domain experts
Monitor token usage and optimization opportunities

Knowledge Base Organization

Properly organized knowledge bases are essential for accurate AI responses and effective information retrieval.

Document Structure Guidelines

Hierarchical Organization


Knowledge Base: Customer Support
├── Product Information/
│   ├── Feature Guides/
│   ├── Technical Specifications/
│   └── Integration Docs/
├── Troubleshooting/
│   ├── Common Issues/
│   ├── Error Codes/
│   └── Resolution Guides/
└── Policies/
    ├── Billing & Refunds/
    ├── Service Level Agreements/
    └── Terms of Service/

Content Quality Standards

Clear, concise titles and headings
Consistent formatting and terminology
Regular content updates and reviews
Proper citation and source attribution
Version control for document changes

Optimization Strategies

Document Size

Optimal: 500-2000 words per document

Avoid: Very large documents that dilute relevance

Metadata Tags

Include: Topic, audience, last updated, confidence level

Example: #billing #enterprise #2024-01

Cross-References

Link: Related documents and concepts

Context: Provide navigation paths between topics

Knowledge Base Maintenance

Monthly Review: Check for outdated information and broken links | Quarterly Audit: Analyze usage patterns and identify gaps | Annual Overhaul: Comprehensive content review and reorganization

Workflow Design Patterns

Follow these proven patterns to create maintainable, reliable, and scalable workflows.

Fundamental Design Principles

Single Responsibility

Each workflow should have one clear purpose and outcome

Easy to understand and maintain

Simpler debugging and troubleshooting

Better reusability across use cases

Fail-Fast Design

Validate inputs and preconditions early in the workflow

Catch errors before expensive operations

Provide clear feedback on failures

Reduce resource waste on invalid inputs

Proven Workflow Patterns

Pattern: Input Validation Gateway


Step 1: Input Validation
  - Check required fields exist
  - Validate data formats and types
  - Verify business rule compliance
  If validation fails: Exit with clear error message

Step 2: Main Processing
  - Execute core business logic
  - Call AI agents with validated inputs
  - Process results and transformations

Step 3: Output Formatting
  - Format results for target system
  - Apply business rules to outputs
  - Generate audit trail entries

Saga Pattern

For multi-system transactions requiring compensation

Execute compensating actions on failure

Maintain data consistency across systems

Handle partial failure scenarios gracefully

Circuit Breaker

Protect against cascading failures in external systems

Fail fast when external services are down

Automatic recovery when services restore

Graceful degradation with fallback options

Workflow Naming Conventions


# Descriptive naming pattern
[Department]_[Process]_[Trigger]_[Version]

Examples:
- CustomerSupport_TicketClassification_FileUpload_v2
- Sales_LeadQualification_WebForm_v1  
- HR_ResumeScreening_EmailAttachment_v3
- Finance_InvoiceProcessing_Scheduled_v1

# Agent naming pattern
[Domain]_[Capability]_Agent

Examples:
- Legal_ContractAnalysis_Agent
- Marketing_ContentGeneration_Agent
- Technical_CodeReview_Agent

Security Guidelines

Implement comprehensive security controls to protect sensitive data and maintain compliance.

Data Classification Framework

Classification	Description	AI Processing	Storage Requirements	Access Controls
Public	Openly available information	No restrictions	Standard storage	All users
Internal	Company-internal information	Internal AI models only	Encrypted at rest	Organization members
Confidential	Sensitive business information	Approved models with audit trail	Encrypted + access logging	Need-to-know basis
Restricted	Personal/regulated data	On-premises only	Encrypted + key management	Authorized personnel only

Security Implementation Checklist

Access Control

Implement role-based access control (RBAC)
Use principle of least privilege
Regular access reviews and cleanup
Multi-factor authentication for admin accounts
API key rotation policies

Data Protection

Encrypt sensitive data at rest and in transit
Implement data loss prevention (DLP)
Regular backup and recovery testing
Data retention and deletion policies
Secure key management practices

Compliance Considerations

GDPR: Implement data subject rights and consent management | HIPAA: Apply technical safeguards and access controls | SOX: Maintain audit trails and financial data controls | PCI DSS: Secure payment card data handling

Performance Optimization

Optimize your AI workflows for speed, cost-effectiveness, and scalability.

Performance Optimization Strategies

Token Optimization

Techniques:

Prompt compression and templating
Context window management
Efficient variable interpolation
Output format optimization

Caching Strategies

Cache Types:

Knowledge base embeddings
API response caching
Workflow result caching
User session data

Parallel Processing

Opportunities:

Independent workflow steps
Batch document processing
Multiple AI model calls
Data validation steps

Monitoring and Metrics

Key Performance Indicators

Execution Metrics

Workflow Duration: Total execution time

Step Performance: Individual step timing

Queue Times: Time waiting for execution

Cost Metrics

Token Usage: LLM costs per workflow

API Calls: External service usage

Compute Resources: Processing costs

Quality Metrics

Success Rate: Successful vs failed executions

Accuracy: Output quality assessment

User Satisfaction: Feedback and ratings

Monitoring & Debugging

Implement comprehensive monitoring to ensure reliable operation and quick issue resolution.

Logging Best Practices

Structured Logging


{
  "timestamp": "2024-01-15T10:30:00Z",
  "level": "INFO",
  "workflow_id": "wf_customer_support_001",
  "step_id": "sentiment_analysis",
  "user_id": "user_12345",
  "message": "Sentiment analysis completed",
  "metadata": {
    "execution_time_ms": 1250,
    "tokens_used": 450,
    "confidence_score": 0.87
  }
}

Error Tracking


{
  "timestamp": "2024-01-15T10:32:15Z",
  "level": "ERROR",
  "workflow_id": "wf_document_process_003",
  "error_type": "ExternalAPIError",
  "error_message": "Document service timeout",
  "stack_trace": "...",
  "context": {
    "retry_attempt": 2,
    "max_retries": 3,
    "api_endpoint": "/api/v1/analyze"
  }
}

Alerting Strategy

Critical Alerts

Immediate Response Required

System downtime
Security breaches
Data corruption
High error rates (>10%)

Warning Alerts

Investigation Needed

Performance degradation
Unusual usage patterns
Resource threshold breaches
Failed integrations

Info Alerts

Awareness Only

Scheduled maintenance
Configuration changes
Usage milestones
System updates

Debugging Workflow

Identify the Issue Review error logs and user reports to understand the problem
Isolate the Component Determine which workflow step or integration is causing the issue
Reproduce the Problem Test with similar inputs to confirm the issue and understand scope
Implement and Test Fix Apply fix in development environment and validate solution

Team Collaboration

Establish effective collaboration practices for teams building and maintaining AI workflows.

Development Workflow

Environment Strategy

Development: Individual testing and experimentation

Staging: Team integration and user acceptance testing

Production: Live workflows with full monitoring

Change Management

Version control for workflow definitions
Code review process for complex workflows
Automated testing and validation
Staged rollout for critical changes

Documentation Standards

Workflow Documentation Template


# Workflow: Customer Support Ticket Classification

## Purpose
Automatically classify incoming support tickets by urgency and department

## Inputs
- Support ticket text
- Customer information
- Historical context

## Outputs  
- Urgency level (High/Medium/Low)
- Department assignment
- Initial response template

## Dependencies
- Customer Support Knowledge Base
- CRM Integration (MCP Server)
- Notification System API

## Maintenance
- Owner: Customer Success Team
- Review Schedule: Monthly
- Last Updated: 2024-01-15
- Next Review: 2024-02-15

Knowledge Sharing

Regular Reviews

Weekly team reviews of workflow performance and user feedback

Best Practice Sharing

Monthly sessions to share successful patterns and lessons learned

Documentation Culture

Maintain up-to-date documentation as part of the development process

Team Roles and Responsibilities

Workflow Architect: Design complex workflow patterns and integration strategies | AI Engineer: Optimize prompts and agent configurations | DevOps Engineer: Manage deployments and monitoring | Business Analyst: Define requirements and validate outcomes

Quick Reference Checklist

🚀 Deployment Checklist

Workflows tested in staging environment
Error handling and fallback paths validated
Performance benchmarks established
Monitoring and alerting configured
Documentation updated and reviewed
Team training completed
Rollback plan documented

🔧 Maintenance Checklist

Regular knowledge base content updates
Workflow performance monitoring
User feedback collection and analysis
Security audit and access review
Cost optimization analysis
Integration health checks
Backup and recovery testing