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Best Practices

Table of Contents

Defining Your Use Case

Before beginning any memory tuning work, clearly articulate what you want the model to do by addressing these key areas:

Task Definition

  • What specific task or problem will the model solve?
    • Example: "Classify customer support tickets by urgency" or "Generate product descriptions from specifications"

Example Use Case: Legal Document Analysis

Success Metrics

  • How will success be measured?
    • Example: "95% classification accuracy on test set" or "Average response time under 2 seconds"
  • Define clear metrics (accuracy, response time, user satisfaction, etc.)
    • Example: "F1 score for urgency classification" or "ROUGE scores for summary quality"
  • Set target performance levels
    • Example: "Minimum 90% accuracy across all categories" or "Maximum 5% false urgents"
  • Plan how to validate results
    • Example: "Weekly manual review of 100 random predictions" or "A/B testing with current process"

Business Impact

  • How will this improve existing processes?
    • Example: "Reduce ticket routing time by 75%" or "Eliminate manual classification backlog"
  • What new opportunities will it create?
    • Example: "24/7 automated ticket prioritization" or "Real-time urgency alerting system"
  • What are the expected cost savings or revenue increases?
    • Example: "Reduce support staff overtime by 30%" or "Increase customer satisfaction by 25%"

Constraints and Requirements

  • Budget limitations
    • Example: "Maximum compute cost of $1000/month" or "One-time training budget of $5000"
  • Technical infrastructure requirements
    • Example: "Must run on existing cloud infrastructure" or "Maximum latency of 100ms"
  • Data privacy and security considerations
    • Example: "No PII storage" or "HIPAA compliance required"
  • Response time requirements
    • Example: "Classification within 5 seconds" or "Batch processing within 1 hour"
  • Accuracy thresholds
    • Example: "99.9% accuracy for high-urgency tickets" or "Maximum 1% false positives"

Stakeholder Analysis

  • Who will use the model?
    • Example: "Front-line support staff" or "Customer service managers"
  • Who will maintain it?
    • Example: "ML Operations team" or "Support engineering group"
  • Who needs to approve it?
    • Example: "Security compliance team" or "Customer support director"
  • Who will be impacted by it?
    • Example: "Customer support representatives" or "End customers waiting for responses"

Finding Relevant Data

Gather examples that represent your intended use case. Look for existing data if enhancing current processes, or create new data for novel applications.

Example Scenario: Customer Support Ticket Classification

For existing processes: - Collect historical tickets with manual urgency classifications - Include resolution times and outcomes - Analyze existing classification patterns

For new opportunities: - Create synthetic examples with sample support tickets - Engage support staff to label historical data - Research public datasets from similar industries

Starting Memory Experiments

Manual Task Analysis

Begin by performing the task yourself to understand the process thoroughly.

Document: - Your step-by-step approach - Required information sources - Reasoning methodology

Evaluation Framework

Develop a robust evaluation strategy combining:

Qualitative Metrics: - Response quality - User satisfaction

Quantifiable Components: - Factual correctness - Source alignment

Note: Always validate metric correlations with stakeholders.

Risk Assessment

Consider potential failure points: - Input variations that could cause confusion - Edge cases and corner cases - Ambiguous scenarios requiring judgment - Common error patterns - Security and safety considerations

Complexity Analysis

Evaluate task difficulty across multiple dimensions: - Required domain knowledge - Process complexity - Context dependencies - Information freshness requirements - Reasoning depth

Task Dimensioning

Break down the task into measurable components:

Input Complexity: - Length and format variations - Information quality and completeness - Input source diversity

Processing Requirements: - Required reasoning steps - Decision dependencies - External knowledge needs

Output Specifications: - Format requirements - Consistency needs - Validation criteria

These components provide a framework for automated metric tracking and evaluation.