Overview
Crafting effective prompts enables you to extract deeper insights from your data and get the answers you need faster. Learn how to structure your questions, choose the right context level, and use different prompt types helps you unlock the full power of Dovetail’s AI.
Understanding Chat Context Levels
Dovetail’s contextual chat automatically applies filters based on your current location, giving you distinct analysis scopes. The filter icon indicates your current context level and can be toggled on/off.
| Context Level | Context | Use Cases | Example |
|---|
| Object level (Micro Analysis) | Focus on individual transcripts, notes, documents, insights, etc | Perfect for detailed insights and specific customer understanding. Works best for most types of questions due to focused scope. | ”What specific concerns does this customer raise about our pricing?” |
| Project or Channel level (Meso Analysis) | Synthesize across all data within a specific project or channel | Requires very specific questions to avoid vague results. Use specific keywords/names in your queries. Works best when you need insights aggregated across the project or channel. | ”What did users say about our checkout process?” (instead of “What are common themes?”) |
| Workspace or Folder level (Macro Analysis) | Query across all projects and data in your folder or workspace | Works best when you need insights aggregated across a folder or workspace. Use specific keywords/names in your queries as broad questions can produce vague results with large datasets. | ”What are users’ top pain points with our search experience?” (instead of “What are top pain points?”) |
Types of Prompts
Contextual chat supports various types of prompts beyond simple questions. Understanding these different approaches will help you get more value from the tool.
Direct questions
The most straightforward way to interact with your data.
Simple Questions:
- “What features do customers request most often?”
- “How do users describe our customer support?”
Analytical Questions:
- “What patterns emerge in churn feedback about our billing system?”
- “What underlying issues cause customers to contact support about integrations?”
- “What factors influence customer satisfaction with our onboarding process?”
Synthesis
Ask chat to combine and connect information from multiple sources to create comprehensive insights.
Within a Project (synthesizing across data objects):
- “Synthesize all feedback about our checkout process”
- “What patterns emerge across all user testing sessions about navigation issues?”
- “Connect customer goals, pain points, and feature requests mentioned throughout this project”
Across Projects (synthesizing within a folder/workspace):
- “Synthesize the main themes about pricing across all customer conversations”
- “What patterns appear in how users describe our onboarding experience?”
- “Connect insights about customer success factors across all projects”
Summarization
Executive Summaries:
- “Summarize the key findings from this project in 3 bullet points”
- “Provide a high-level overview of customer sentiment about our latest release”
- “What are the top 3 takeaways from all customer interviews this quarter?”
Detailed Summaries:
- “Provide a comprehensive summary of all usability issues mentioned in testing sessions”
- “Summarize customer feedback about our mobile app, including specific examples and quotes”
- “Give me a detailed breakdown of all feature requests with context about why users want them”
Thematic Summaries:
- “Summarize feedback organized by customer journey stage”
- “Group all pricing feedback into themes and summarize each theme”
- “Summarize what customers say about our product organized by feature area”
Tasks
Creating Deliverables:
- “Draft a follow-up email for this call”
- “Create a list of product improvements to share with the engineering team based on this feedback”
- “Write a brief for our design team about the navigation concerns users expressed”
- “Generate talking points for our next customer advisory board about feature priorities”
Analysis Tasks:
- “Identify the root causes of customer churn”
- “Extract all mentions of competitor products and what users say about them”
Custom Personas:
- “Create a profile of our power users based on what they say in interviews”
- “Build a persona for enterprise buyers based on sales conversation data”
Prioritization Help:
- “Rank feature requests by frequency and impact based on customer feedback”
- “Prioritize usability issues by how often they’re mentioned and severity”
- “Order customer pain points by business impact based on what customers say”
Report Creation:
- “Summarize issues to share with the product team, organized by priority”
- “Generate a report on customer satisfaction trends with supporting quotes”
- “Compile all feedback about our onboarding process into a structured document”
Crafting Effective Prompts
The quality of your answer depends entirely on the quality of your question. The broader your scope (from a single data object to an entire workspace), the more specific and targeted your prompt needs to be to get clear, actionable results.
The Specificity Principle
The system performs searches based on your language, so use specific keywords/names that would actually appear in your data.
Generic questions produce vague results:
- ❌ “What do customers think?”
- ✅ “What specific concerns do customers express about our onboarding process?”
Be specific about topics:
- ❌ “What are common themes?”
- ✅ “What did customers say about our mobile app performance?”
Use customer language:
- ❌ “Any insights about pain points?”
- ✅ “What frustrations do users describe when trying to complete their profile setup?”
Use Natural Language
Frame prompts using words your customers would actually use:
- “What problems do customers mention with billing?”
- “How do users describe the signup process?”
- “What complaints appear about our customer support?”
- “What do customers like about our dashboard?”
Filtering by Custom Fields
You can narrow your results by referencing custom fields in your queries.
| Field Type | Structure | Example |
|---|
| Text Field | Use data where [Field Name] is [value] | Only include documents where Status is Active |
| Select/Dropdown Field | Only include data where [Field Name] is [option] | Use interviews where Product is Mobile App |
| Boolean Field | Only show data where [Field Name] is true/false | Use documents where Published is true |
| Date Field | Only include data where [Field Name] is [date/date range] | Use interviews where Interview Date is after January 1, 2024 |
- “What feedback do we have where Priority is High?”
- “Only include notes where Customer Type is Enterprise”
Multiple field filters:
- “Use interviews where Product is Web App and Status is Completed”
- “Only include notes where Region is North America and Published is true”
Combined with search terms:
- “Find feedback about login issues where Severity is Critical”
- “What do customers say about pricing? Only include data where Product is SaaS”
- “List the top 5 pain points mentioned in customer calls”
- “Compare mobile app feedback vs. web app feedback”
- “Summarize billing issues in bullet points”
- “Which issues have the highest impact on customer satisfaction?”
- “What themes emerged in Q4 customer interviews?”
Response Length and Detail
For longer, more comprehensive responses:
- Use explicit detail requests: “Provide a detailed analysis of…” or “Give me a comprehensive explanation of…”
- Ask for multiple aspects: “What are the main themes, specific examples, and patterns?”
- Request structured formats: “Break this down with headings and bullet points”
For refined results:
- Follow up if needed: “Can you expand on that?” or “Provide more detail about [specific aspect]”
- Ask chat to reorganize: “Can you reformat that as a table?” or “Group those by priority”
Crafting your Project Overview
Your project overview becomes part of the AI’s context, helping it understand the project’s purpose, scope, and domain. A well-crafted overview improves search quality and answer relevance throughout your conversations.
What to Include
1. Project Purpose & Goals — Primary research questions, business context, expected outcomes.
2. Key Terminology & Domain Context — Industry-specific terms, product/service names, customer personas, acronyms.
3. Data Sources & Types — Types of data (interviews, surveys, support tickets), collection methods, time periods, geographic scope.
4. Research Methodology — How data was collected, key stakeholders, important dates, frameworks used.
5. Key Themes & Topics — Main themes or tags, important patterns already identified, areas of focus.
Workspace Chat Customization
Available on the Enterprise plan
What You Can Customize
1. Role and Persona — Define the assistant’s role, set expertise areas, specify the perspective to take.
2. Response Style and Tone — Formality level, voice, tone, language preferences.
3. Formatting and Structure — Preferred structure, heading usage, citation format, length preferences.
4. Content Focus and Priorities — What to emphasize or de-emphasize, domain-specific priorities.
5. Rules and Constraints — What to include or exclude, terminology preferences, naming conventions.
6. Domain-Specific Guidance — Industry terminology, compliance requirements, research methodology preferences.
Example Configuration
You are a UX research assistant specializing in B2B SaaS products.
Focus Areas:
- Prioritize insights about product usability and workflow efficiency
- Emphasize quantitative data when available
- Always consider enterprise security and compliance requirements
Response Style:
- Use a professional but approachable tone
- Structure responses with clear headings and bullet points
- Keep responses concise (prefer SHORT to MEDIUM length)
Terminology:
- Use "customers" not "users"
- Use "features" not "functionality"
- Always refer to "workspaces" not "accounts"
Content Rules:
- Never mention specific competitor products by name
- Always anonymize customer names in responses
- Focus on actionable insights that can drive product decisions
Persona-based Examples
Researcher
| Context Level | Example Prompts |
|---|
| Object level | ”What usability issues does this participant encounter during task completion?” / “Identify the root cause of this user’s confusion with the interface” |
| Project level | ”Summarize all usability issues found in this testing round with severity levels” / “Create a research summary to share with the product team highlighting critical issues” |
| Workspace level | ”What usability patterns emerge across all research projects this year?” |
Product Manager
| Context Level | Example Prompts |
|---|
| Object level | ”What feature requests does this customer mention and why?” / “Rank this customer’s feature requests by urgency” |
| Project level | ”Summarize feature requests with customer impact for our roadmap discussion” / “How do customer needs differ between enterprise and SMB segments?” |
| Workspace level | ”Rank all feature requests by frequency and business impact” / “Create a quarterly product insights report” |
Designer
| Context Level | Example Prompts |
|---|
| Object level | ”What design language does this customer use to describe their preferences?” |
| Project level | ”Summarize all visual design feedback with specific UI improvement suggestions” / “Create a design brief based on user feedback about the dashboard” |
| Workspace level | ”What design system improvements would address user feedback across all projects?” |
Customer Success
| Context Level | Example Prompts |
|---|
| Object level | ”What onboarding challenges does this customer face?” / “Draft a follow-up email addressing this customer’s support concerns” |
| Project level | ”Summarize adoption barriers with recommendations for improving onboarding” / “Create a health score analysis based on customer feedback themes” |
| Workspace level | ”What are the leading indicators of customer success based on all feedback?” |
Sales
| Context Level | Example Prompts |
|---|
| Object level | ”What objections does this prospect raise?” / “Create talking points to address this prospect’s integration concerns” |
| Project level | ”Summarize competitive objections with recommended responses” / “Create a sales battlecard based on common objections and win themes” |
| Workspace level | ”What messaging themes appear in our most successful sales conversations?” |
