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.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?”
- “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”
- “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?”
- “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”
- “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”
- “Identify the root causes of customer churn”
- “Extract all mentions of competitor products and what users say about them”
- “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”
- “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”
- “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?”
- ❌ “What are common themes?”
- ✅ “What did customers say about our mobile app performance?”
- ❌ “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.
Single field filter:
- “What feedback do we have where Priority is High?”
- “Only include notes where Customer Type is Enterprise”
- “Use interviews where Product is Web App and Status is Completed”
- “Only include notes where Region is North America and Published is true”
- “Find feedback about login issues where Severity is Critical”
- “What do customers say about pricing? Only include data where Product is SaaS”
Formatting Your Responses
- “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”
- 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