Best Practices

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Optimizing Your Raycaster Experience

This guide provides best practices and recommendations to help you maximize productivity, maintain data quality, and achieve better results with Raycaster AI Workspace. These guidelines are based on customer feedback and insights from power users.

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Workspace Organization

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Structuring Your Sheets

For effective data organization:

• Purpose-Specific Sheets: Create separate sheets for different research objectives rather than one massive sheet
• Logical Naming: Use clear, descriptive names for sheets (e.g., “Q2 2023 Target Companies” instead of “Sheet 1”)
• Template Utilization: Start with templates for common use cases rather than building from scratch
• Consistent Structure: Maintain similar column structures across related sheets for easier cross-reference
• Size Management: Keep sheets under 50,000 rows for optimal performance

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Column Design

Principles for effective column configuration:

• Strategic Ordering: Place most frequently used columns first
• Logical Grouping: Keep related columns adjacent (e.g., all contact information together)
• Progressive Detail: Arrange from general to specific (e.g., company name → industry → specific technologies)
• Minimizing Width: Set appropriate column widths to maximize visible data
• Consistent Naming: Use clear, consistent naming conventions (e.g., “Founded Year” not just “Year”)

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View Management

Leverage views for different workflows:

• Role-Based Views: Create views optimized for different team roles
• Process-Specific Views: Design views for specific workflows (research, outreach, analysis)
• Presentation Views: Clean views for sharing/presenting to stakeholders
• Analysis Views: Detail-rich views for deep research and analysis
• Default View Selection: Set appropriate default views for different sheets

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Research Optimization

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Writing Effective Prompts

Guidelines for creating research prompts that yield better results:

• Clear Objectives: State exactly what information you need
• Specific Context: Provide relevant background when needed
• Output Format: Specify how you want information structured
• Relevance Guidance: Indicate what makes information valuable for your needs
• Variable Utilization: Use column variables effectively to personalize research

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Column Type Selection

Choose the optimal column type for different kinds of research:

Data Type	Best Column Type	Example Use Case
Detailed analyses	TEXT with rich formatting	Company overviews, technology assessments
Categorization	SINGLE_SELECT	Development stage, primary therapeutic area
Multiple attributes	MULTI_SELECT	Multiple indications, technology platforms
Specific data points	NUMBER with formatting	Funding amounts, employee counts
Binary determinations	BOOLEAN	Patent existence, public company status
Linked entities	ACTION	Key executives, parent companies

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Research Efficiency

Strategies for more efficient research operations:

• Batched Research: Process similar items together rather than individually
• Progressive Research: Start with basic information before diving deep
• Scheduled Operations: Run large research batches during off-hours
• Targeted Scope: Research only what you need rather than everything possible
• Template Refinement: Continuously improve prompt templates based on results

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Data Quality Management

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Standardization Practices

Maintain consistent data formats for better analysis and research:

• Date Standardization: Use consistent date formats (YYYY-MM-DD recommended)
• Name Formatting: Standard capitalization and formatting for names
• URL Consistency: Standard URL formats (with or without “https://”, but not mixed)
• Currency Handling: Consistent currency notation with proper numerical formatting
• Enumeration Control: Standardized lists for categorical data

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Data Validation

Techniques to ensure data accuracy:

• Import Validation: Carefully review data during import mapping
• Spot Checking: Regularly verify a sample of AI research results
• Duplicate Detection: Periodically check for and merge duplicate entries
• Reference Verification: Validate research against trusted external sources
• Peer Review: Implement collaborative review for critical data

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Data Enrichment Strategy

Smart approaches to data enrichment:

• Priority Targeting: Focus enrichment on high-value prospects first
• Progressive Enhancement: Start with basic info, then add detail over time
• Freshness Management: Schedule regular updates for time-sensitive data
• Selective Depth: Use deep research only where it adds significant value
• Cross-Validation: Compare data from multiple research methods

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Collaboration Best Practices

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Team Workflows

Guidelines for effective team collaboration:

• Role Definition: Clearly define who is responsible for what data
• Process Documentation: Document standard procedures for common tasks
• Update Protocols: Establish when and how data should be updated
• Change Communication: Use comments to explain significant data changes
• Handoff Procedures: Create clear processes for transferring ownership

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Permission Management

Best practices for access controls:

• Principle of Least Privilege: Grant only the permissions needed for each role
• Regular Audits: Periodically review and update user permissions
• Role-Based Access: Use consistent permission templates for similar roles
• Sensitive Data Protection: Apply column-level permissions for sensitive information
• External Sharing Controls: Use time-limited, read-only access for external stakeholders

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Communication Integration

Effective use of communication tools:

• Contextual Discussions: Use row and cell comments for specific discussions
• Reference Linking: Include direct links to specific rows in messages
• Status Updates: Use status columns to communicate progress visually
• Change Logging: Document significant changes in dedicated columns
• Alert Configuration: Set up notifications for important changes

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Performance Optimization

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Resource Efficiency

Strategies for optimal system performance:

• Visible Data Limiting: Show only necessary columns in each view
• Filter Application: Apply filters before performing operations on large datasets
• Batch Processing: Use batch operations instead of individual updates
• Background Research: Schedule intensive research during off-hours
• Regular Cleanup: Archive or delete unused or outdated data

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Query Optimization

Tips for faster filters and sorts:

• Simple Filters First: Apply simple filters before complex ones
• Indexed Column Usage: Prioritize filtering on indexed columns
• Query Complexity Management: Break complex filters into simpler steps
• Sort Order Planning: Sort by commonly-used fields for faster access
• View Saving: Save complex filter/sort combinations as views

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Advanced Techniques

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Formula Utilization

Leveraging formulas for derived data:

• Automated Concatenation: Combine data from multiple columns automatically
• Conditional Formatting: Use formulas to determine display formatting
• Validation Rules: Create formulas for data validation
• Calculated Metrics: Derive KPIs and metrics automatically
• Dynamic References: Build cross-references between sheets

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AI Assistant Integration

Best practices for AI assistant usage:

• Task Delegation: Use the assistant for repetitive or formulaic tasks
• Content Refinement: Have the assistant polish or format research results
• Data Summarization: Ask for concise summaries of detailed research
• Pattern Identification: Request analysis of trends across multiple rows
• Process Documentation: Use the assistant to create process documentation

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Automation Workflows

Guidelines for effective automation:

• Process Assessment: Identify repetitive tasks for automation
• Trigger Accuracy: Choose precise triggers for automated actions
• Progressive Testing: Test automations with small batches first
• Monitoring Plan: Set up clear monitoring for automated processes
• Iteration Schedule: Review and refine automations regularly

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Industry-Specific Recommendations

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Life Sciences Research

Specialized tips for life sciences and biotech companies:

• Therapeutic Area Standardization: Use consistent terminology across sheets
• Development Stage Tracking: Create standardized pipeline stage definitions
• Regulatory Status Monitoring: Dedicated columns for regulatory milestones
• Patent Landscape Analysis: Structured approach to patent research
• Scientific Collaboration Mapping: Track research partnerships systematically

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Sales and BD Teams

Optimizing for business development workflows:

• Engagement Tracking: Structured process for tracking customer interactions
• Opportunity Scoring: Consistent methodology for prospect prioritization
• Competitive Intelligence: Organized approach to competitor monitoring
• Deal Stage Visualization: Clear visual indicators of deal progress
• Customer Journey Mapping: Track progression through defined sales stages