Genspark Conversation Design: How to Significantly Improve Productivity with Effective Prompt Structures

Introduction: The Time Waste Caused by Ambiguous Instructions

If you've ever asked Genspark to "create a login feature," were you satisfied with the result? Generally, such ambiguous instructions inevitably lead to disappointing outcomes and require multiple reworks.

Many developers have experienced spending a full day implementing a login feature, with only a few hours actually coding, and the rest caught in an infinite loop of 'not what I expected' → 'request for correction' → 'still not right.' This highlights the importance of structuring conversations with Genspark.

Three Reworks Caused by 'Just Make It'

A common pattern in the early stages of a project is making simple requests to Genspark, such as the following:

Please implement a user login feature.

Genspark kindly generated the code, but it had the following issues:

Issues with the First Output

• ❌ Authentication method unclear (Session? JWT?)
• ❌ No password hashing
• ❌ Insufficient error handling
• ❌ Database connection code not included

So, I requested a correction.

Please implement a login feature using JWT authentication.
Hash passwords with bcrypt.

Issues with the Second Output

• ✅ JWT authentication was implemented
• ✅ Password hashing was also implemented
• ❌ No refresh token
• ❌ Token expiration not set
• ❌ Database schema different from expectations

I requested further corrections, and finally achieved a satisfactory result on the third attempt. However, if **the requirements had been clearly communicated from the start, it would have been completed in one go**.

Case Study 1: Achieving 80% First-Attempt Success Rate with Structured Prompts

From such failures, the importance of **structured prompt templates** becomes clear. Using these templates can significantly improve the first-attempt success rate.

# Task Overview
[What to build, concisely in 1-2 sentences]

# Requirements Definition

## Functional Requirements
- [Mandatory feature 1]
- [Mandatory feature 2]
- [Optional feature 1]

## Non-Functional Requirements
- Security: [Specific requirements]
- Performance: [Specific requirements]
- Error Handling: [Specific requirements]

# Technical Stack
- Language/Framework: [Including specific versions]
- Database: [Including schema]
- Authentication Method: [Detailed specifications]
- Libraries: [Libraries to use/avoid]

# Constraints
- [Coding conventions]
- [File structure]
- [How to integrate with existing code]

# Expected Output
- [File structure]
- [Granularity of comments]
- [Presence/absence of test code]

# Reference Information
- [Existing similar code]
- [Documentation URL]

Practical Example: Login Feature Implementation

# Task Overview
Implement a user login feature for an e-commerce site. Use JWT authentication to achieve
two-factor authentication with access tokens and refresh tokens.

# Requirements Definition

## Functional Requirements
- Log in with email address and password
- Return access token and refresh token upon successful login
- Token refresh endpoint
- Logout feature (token invalidation)
- (Optional) Login attempt limit

## Non-Functional Requirements
- Security: Passwords to be hashed with bcrypt (salt rounds: 10)
- Performance: Token verification within 1ms
- Error Handling: All errors to be returned in structured JSON

# Technical Stack
- Language/Framework: Node.js 18, Express 4.18
- Database: PostgreSQL 14, Prisma ORM
- Authentication Method: JWT (jsonwebtoken 9.0)
  - Access Token Expiration: 15 minutes
  - Refresh Token Expiration: 7 days
- Libraries: bcrypt 5.1 (do not use bcryptjs)

# Constraints
- ESLint + Prettier compliance
- File Structure: /src/routes/auth.js, /src/controllers/authController.js, /src/middleware/authMiddleware.js
- Integrate with existing Prisma schema (use User, RefreshToken models)
- Return error messages in Japanese

# Expected Output
- File Structure: The 3 files listed above
- JSDoc comments for each function
- Include unit test code (Jest)

# Reference Information
- Prisma Schema: /prisma/schema.prisma
- Existing Error Handling Middleware: /src/middleware/errorHandler.js
- JWT Configuration: /src/config/jwt.config.js

By using such detailed prompts, **Genspark can now generate the expected code on the first attempt**.

Case Study 2: Decomposing Complex Tasks with Phased Conversation Design

For complex tasks, instead of immediately requesting implementation, the success rate increases by **designing conversations in phases**. I adopt a three-phase approach: 'Design Phase' → 'Implementation Phase' → 'Review Phase'.

Phase 1: Design Phase

First, we discuss implementation methods and consider multiple options.

Task: I want to add a feature to record user login history.

Please propose 3 different approaches for the following aspects:
1. Database schema design
2. Log storage timing (synchronous/asynchronous)
3. Log lifecycle management (deletion policy)

For each approach, please describe:
- Advantages
- Disadvantages
- Impact on performance
- Implementation complexity (5-point scale)

Do not write code; only propose design ideas.

At this stage, Genspark provides multiple design proposals. I compare and evaluate them to select the optimal approach.

Phase 2: Implementation Phase

Once the design is finalized, I request detailed implementation.

I will adopt "Approach 2: Asynchronous Log Storage + Redis Queue" which you proposed earlier.

Please implement it with the following specifications:

# Database Schema
- Table Name: user_login_logs
- Columns: id (UUID), user_id (FK), ip_address (INET), user_agent (TEXT), logged_in_at (TIMESTAMP)
- Indexes: user_id, logged_in_at (composite index)

# Implementation Requirements
- Add job to Redis queue upon successful login
- Background worker processes queue every 1 minute
- Retry log saving up to 3 times on failure
- Automatically delete logs older than 90 days (daily cron job)

# File Structure
- /src/services/loginLogService.js
- /src/workers/loginLogWorker.js
- /src/jobs/cleanupLoginLogs.js

Please include test code in each file.

Phase 3: Review Phase

Review the generated code and point out areas for improvement.

I have reviewed the generated code. Please improve the following points:

1. Insufficient error handling in addToQueue function in loginLogService.js
   → Add handling for Redis connection errors

2. No concurrency control for log saving in loginLogWorker.js
   → Limit to a maximum of 10 parallel processes

3. Batch deletion chunk size too large in cleanupLoginLogs.js
   → Limit deletion to 1000 records per run

4. Insufficient mocks in test code
   → Add Redis and database mocks

Please regenerate the code with the above 4 points corrected.
Add "// FIX:" comments to the corrected sections.

This three-phase approach has enabled us to **reliably generate high-quality code even for complex tasks**.

Case Study 3: Building Reusable Prompt Templates

For frequently used tasks, I created **reusable prompt templates** and organized them into a library. This saves the effort of thinking up prompts every time and ensures consistent code generation.

Prompt Template Library Structure

prompt-templates/
├── backend/
│   ├── api-endpoint.md
│   ├── database-model.md
│   ├── middleware.md
│   └── background-job.md
├── frontend/
│   ├── react-component.md
│   ├── custom-hook.md
│   └── api-integration.md
├── testing/
│   ├── unit-test.md
│   ├── integration-test.md
│   └── e2e-test.md
└── README.md

# API Endpoint Implementation Template

## Task Overview
Implement a new API endpoint: {METHOD} {PATH}

## Requirements

### Functional Requirements
- {requirement_1}
- {requirement_2}
- {requirement_3}

### Non-Functional Requirements
- Authentication: {auth_method}
- Authorization: {permission_required}
- Rate Limiting: {rate_limit}
- Response Time: < {max_response_time}ms

## Technical Stack
- Framework: Express {version}
- Database: PostgreSQL + Prisma
- Validation: Joi {version}
- Error Handling: Custom error classes

## Implementation Details

### Request Schema
```json
{request_schema}
```

### Response Schema
```json
{response_schema}
```

### Error Responses
- 400: {validation_error}
- 401: {auth_error}
- 403: {permission_error}
- 404: {not_found_error}
- 500: {server_error}

## File Structure
- Route: /src/routes/{resource}.routes.js
- Controller: /src/controllers/{resource}.controller.js
- Service: /src/services/{resource}.service.js
- Validation: /src/validations/{resource}.validation.js

## Constraints
- Follow existing code style (ESLint + Prettier)
- Use async/await (no callbacks)
- Add JSDoc comments to all functions
- Include error handling for all database operations

## Expected Output
- All 4 files listed above
- Unit tests for service layer
- Integration tests for API endpoint
- OpenAPI/Swagger documentation

## Reference
- Existing similar endpoint: {reference_endpoint}
- Database schema: /prisma/schema.prisma

How to Use the Template

Replace the variable parts ({variable}) with actual values before use.

import fs from 'fs';

class PromptTemplateManager {
  constructor(templateDir) {
    this.templateDir = templateDir;
  }
  
  loadTemplate(category, templateName) {
    const filePath = `${this.templateDir}/${category}/${templateName}.md`;
    return fs.readFileSync(filePath, 'utf-8');
  }
  
  fillTemplate(template, variables) {
    let filled = template;
    
    for (const [key, value] of Object.entries(variables)) {
      const regex = new RegExp(`{${key}}`, 'g');
      filled = filled.replace(regex, value);
    }
    
    return filled;
  }
  
  generate(category, templateName, variables) {
    const template = this.loadTemplate(category, templateName);
    return this.fillTemplate(template, variables);
  }
}

// Usage example
const manager = new PromptTemplateManager('./prompt-templates');

const prompt = manager.generate('backend', 'api-endpoint', {
  METHOD: 'POST',
  PATH: '/api/v1/products',
  requirement_1: 'New product registration',
  requirement_2: 'Product name, price, and stock quantity are mandatory fields',
  requirement_3: 'Up to 5 images can be registered',
  auth_method: 'JWT Bearer Token',
  permission_required: 'products:create',
  rate_limit: '100 requests per hour',
  max_response_time: '200',
  request_schema: JSON.stringify({
    name: 'string',
    price: 'number',
    stock: 'number',
    images: 'string[]'
  }, null, 2),
  response_schema: JSON.stringify({
    id: 'string',
    name: 'string',
    price: 'number',
    stock: 'number',
    images: 'string[]',
    createdAt: 'string'
  }, null, 2),
  validation_error: 'Invalid request body',
  auth_error: 'Missing or invalid token',
  permission_error: 'Insufficient permissions',
  not_found_error: 'Product not found',
  server_error: 'Internal server error',
  resource: 'product',
  reference_endpoint: '/src/routes/user.routes.js'
});

console.log(prompt);

This template system has **reduced prompt creation time by 80%** and improved code consistency.

Conversation Design Framework and Practical Methods

Based on the examples so far, I will summarize the conversation design framework for interacting with Genspark.

1. CRISP Principles (5 Principles of Conversation Design)

• C - Clear: Avoid ambiguous expressions; give specific instructions
• R - Relevant: Provide project context
• I - Incremental: Decompose complex tasks and proceed in stages
• S - Structured: Structure prompts to make them easy to read
• P - Precise: Specify version numbers and numerical values accurately

2. Prompt Checklist

Before submitting a prompt, check the following items:

• ✅ Is the task's purpose clear?
• ✅ Have you specified the technical stack (language, framework, version)?
• ✅ Have you distinguished between functional and non-functional requirements?
• ✅ Have you clearly stated constraints (coding conventions, file structure)?
• ✅ Have you specified the expected output format?
• ✅ Have you provided reference information (existing code, documentation)?

3. Dialogue Patterns

Select the optimal dialogue pattern depending on the task type.

Summary: The Quality of Dialogue Determines the Quality of Output

Interacting with Genspark is not just giving 'instructions,' but is **'design' itself.** Ambiguous instructions lead to ambiguous results, while clear instructions yield clear results.

Achievements through Conversation Design

• First-attempt success rate improved from 30% to 80%
• Rework time reduced by 70%
• Significant improvement in the quality of generated code
• Improved consistency across the team

Actions You Can Start Today

• ✅ Create structured prompt templates
• ✅ Template frequently used tasks
• ✅ Tackle complex tasks with phased dialogue
• ✅ Make the prompt checklist a habit
• ✅ Add successful prompts to your library

These measures will transform interactions with Genspark from **mere 'tasks' to a 'design process,'** dramatically improving development productivity.

Reference Links

• Prompt Engineering Guide - Comprehensive Guide to Prompt Design
• GitHub: Prompt Engineering Guide - Collection of Prompt Patterns
• OpenAI: Prompt Engineering - Official Best Practices
• Anthropic: Prompt Engineering - Claude's Prompt Design
• Learn Prompting - Prompt Engineering Learning Site
• arXiv: Chain-of-Thought Prompting - Phased Thought Prompts
• DeepLearning.AI: Prompt Engineering for Developers - Course for Developers
• GitHub: Awesome ChatGPT Prompts - Collection of Prompt Examples