Prompting 101
Prompting is how you communicate with ToolJet's AI to build internal applications. Think of it as giving detailed instructions to a highly skilled developer who understands the platform but needs context about your specific business needs.
The AI then translates these requirements into a fully functional application using ToolJet's low code application builder.
Why Good Prompting Matters
The Challenge: Generic prompts produce generic applications. Simply asking for "a CRM system" or "project management tool" results in basic, template-like apps that don't reflect your actual business processes.
The Solution: Contextual prompting that provides business background, user needs, and specific workflows produces applications that feel purpose-built for your organization.
Real Impact Example
Generic Prompt
Prompt: "Build a customer management system"
Result: Basic contact forms and lists that could work for anyone.
Contextual Prompt
Prompt: "Our design agency needs to track 50+ concurrent client projects, manage creative approval workflows, and prevent resource conflicts between our 12-person team..."
Result: Specialized project management app with approval workflows, resource calendars, and client communication tools.
What ToolJet Already Handles
Don't waste your prompt describing these - they're built into the platform:
ToolJet Automatically Provides
- User authentication and login systems
- Role-based access control and permissions
- Database integration and data storage
- API integrations with external services
- Security features and data encryption
Don't Include in Your Prompts
- "Build user login and authentication"
- "Add role-based permissions"
- "Create API integrations"
- "Include security features"
Instead Focus On
- Business workflows and processes
- Manual data entry and forms
- Custom business logic
- Specific reporting needs
- User interface requirements
The 3-Section Formula
Structure your prompts with these three sections for best results:
1. Business Context
Explain why you need this tool and what problems it solves.
Good Example
"Our sales team currently tracks leads in spreadsheets across 5 different files, causing data inconsistencies and missed follow-ups. We need centralized lead management that prevents duplicate entries and gives visibility into our sales pipeline."
Avoid
"Build a CRM system."
2. User Personas
Define who will use the tool and what they need to accomplish.
Good Format
- Sales Reps - Need to log new leads, update contact information, and track deal progress
- Sales Managers - Require pipeline visibility, team performance metrics, and forecast reporting
- Marketing Team - Must see lead sources, conversion rates, and campaign effectiveness
3. User Flows & Features
Describe how users will interact with the tool through specific workflows.
Good Format
- Lead Capture Flow - Import leads from various sources, assign to sales reps, set follow-up reminders
- Pipeline Management Flow - Move deals through stages, update probabilities, log interactions
- Reporting Flow - Generate weekly pipeline reports, track conversion metrics, analyze lead sources
Efficient Utilization of Credits
Do This
- Write Specific Prompts with Detailed Context
A precise prompt costs fewer credits because the AI resolves ambiguity in fewer back-and-forth steps. Instead of "Build me a dashboard to track new user signups every day," include your business context, the problem you're solving, and what the output should look like: "Build me a dashboard to track new user signups every day. I need a daily signups chart for the last 30 days, a summary of total users this month vs last month, and a table showing each new user's name, email, signup date, and plan type. Let me filter by date range and plan." this spends credits on building, not on clarifying. - Keep Prompts Concise
Every word in your prompt is processed at a cost. Aim for ~500 words or less. Pasting full PRDs or spec documents inflates credit usage without improving output, extract only what's relevant to the task at hand. - Keep the Number of Tasks Per Prompt Minimal
Bundling multiple requirements into one prompt forces the AI to reason over a larger scope, consuming more credits and often producing lower-quality output. Send one focused task at a time. - Split Operations into Smaller Tasks
A single large operation uses more credits and is harder to course-correct if something goes wrong. Breaking work into smaller steps means you spend credits only on what you've validated, not on reworking a bloated result. - Start a New Conversation for Different Tasks
The full context of a conversation is passed with each new message, so the longer the history, the more credits each turn costs. Once you've finished a task, start a fresh conversation rather than continuing the same thread for unrelated work.
Avoid This
- Vague prompts that require multiple follow-up turns to clarify, each turn costs credits
- Feature lists without context ("needs forms, tables, and reports"), the AI spends credits guessing intent
- Pasting full PRDs or long specification documents, excess tokens add cost with no quality benefit
- Bundling unrelated tasks into one prompt, broader scope means more processing and higher credit use
- Over-specifying UI layout details, this adds prompt length and cost without meaningful impact, since the AI handles design decisions
How Long Should My Prompt Be?
Short Prompts (2-3 sentences per section)
- Best for: Simple tools with straightforward workflows
- Risk: May lack necessary detail for complex business logic
Medium Prompts (1 paragraph per section)
- Best for: Most internal tools
- Sweet spot: Provides enough context without overwhelming
- This length typically produces the most usable results
Long Prompts (2-3 paragraphs per section)
- Best for: Complex workflows with multiple user types
- Risk: May create overly complicated interfaces
Specific prompts use fewer AI credits. Vague prompts make the AI work harder, consuming more credits as it tries to fill in the gaps.
Scoping Your Application
Start Small, Then Iterate
Begin with a simple version (3-4 pages) covering core workflows, then build additional features. This approach:
- Makes the application easier to test and refine
- Reduces complexity and potential errors
- Allows you to validate workflows before expanding