Accelerating Enterprise Development
A Case Study on AI-Powered Liferay Code Generation with Retrieval-Augmented Generation
Project Goal
The aim of this Project to develop an AI-powered Liferay code generation assistant capable of producing production-ready Java and Groovy code tailored to Liferay 7.4 DXP. The tool would leverage Retrieval-Augmented Generation (RAG) techniques and advanced prompt engineering to assist developers in writing high-quality, standards-compliant Liferay modules and scripts, dramatically reducing development time and errors.
Industry
Enterprise Portal Development
Location
San Francisco
Tech Team
Project Manager | AI Developer Engineer | Liferay Architect | Full Stack Developer (Python, Streamlit) | Prompt Engineer | DevOps Engineer
Team Involved
- Project manager
- Data Scientists
- ML Engineers
- Risk Assessment Specialists
- Full Stack Engineers (React/Python)
- Compliance Officers
- UX Designer
Business Tasks the Client Wanted to Solve
Time-Intensive Liferay Development
- Manually writing boilerplate code for modules, services, and scripts consumed significant developer time
Lack of Liferay-Specific AI Tools
- General-purpose AI tools lacked domain knowledge of Liferay APIs, modules, and structure.
Compliance with Liferay Standards
- Ensuring generated code strictly adhered to Liferay 7.4 DXP guidelines was a consistent challenge.
High Onboarding Time
- New developers faced steep learning curves when writing Liferay-compliant Java and Groovy code.
Business Tasks the Client Wanted to Solve:
1) Accelerate Loan Processing
- Reduce the time taken to process and approve loan applications through automation of manual review processes
- Enable real-time preliminary loan decisions
2) Enhance Risk Assessment
- Implement more sophisticated risk assessment models using multiple data points
- Improve the accuracy of default prediction
- Reduce human bias in the loan approval process
3) Change Any Particular Thing in the Generated Image
- Handle increasing application volumes without proportionally increasing staff
- Maintain consistency in loan evaluations across all applications
- Enable simultaneous processing of multiple applications.
4) Ensure Compliance
- Maintain transparent decision-making processes
- Provide clear audit trails for regulatory requirements
Implement fair lending practices
5 ) Improve Customer Experience
- Reduce waiting times for loan decisions
- Provide clear feedback on application status
- Enable digital document submission and verification
What pitfalls did the client face?
Lack of Standardized Datasets
Existing datasets were insufficient to cover diverse fighting scenarios.
Low Accuracy in Real-Time Detection
Previous systems failed to achieve high accuracy in detecting fighting scenes in varied lighting and environmental conditions.
Integration Challenges
Difficulties in integrating AI systems with legacy surveillance infrastructure.
Data Privacy Concerns
Ensuring that surveillance data remains secure and complies with privacy regulations.
What pitfalls did the client face?
1) Legacy System Integration
- Existing systems were not designed for AI integration
- Historical data was stored in various formats and locations
- Manual processes were deeply embedded in operations
2) Data Quality and Standardization
- Inconsistent data formats across different sources
- Missing or incomplete historical data
- Lack of standardized documentation processes.
3) Regulatory Compliance
- Need for explainable AI decisions
- Ensuring fair lending practices
- Meeting data privacy requirements
4) Staff Resistance
- Concerns about job security
- Reluctance to adopt new technologies
- Learning curve for new systems
What we suggested
AI-Powered Dual Code Generation
- Built a Streamlit-based chatbot capable of generating Java and Groovy code tailored to Liferay 7.4 DXP.
- Leveraged Groq API with RAG-based prompt injection for contextually accurate code generation.
Retrieval-Augmented Generation (RAG) Pipeline
- Embedded Liferay’s actual source code using Sentence Transformer.
- Enabled semantic retrieval of relevant snippets from a persistent Chroma DB vector store.
Smart Prompt Engineering
- Developed prompt templates enforcing strict Liferay compliance.
- Used external prompt files to dynamically construct LLM inputs for both Java and Groovy code use cases.
Smart Prompt Engineering
- Developed prompt templates enforcing strict Liferay compliance.
- Used external prompt files to dynamically construct LLM inputs for both Java and Groovy code use cases.
Fault-Tolerant API Integration
- Integrated retry and exception handling logic for LLM API requests via Groq.
Modern, Developer-Friendly UI
- Branded UI supporting dark mode, logo customization, and mode-switching between Java and Groovy generation.
What we suggested:
1. Initial Assessment and Planning
- Conduct thorough analysis of existing loan approval processes
- Identify key pain points and automation opportunities
- Define success metrics and compliance requirements
- Create implementation roadmap
2. System Design and Architecture
- Backend: Use Python and TensorFlow for AI model development. Leverage cloud services for scalability and data management.
- Frontend: React-based dashboard for loan officers
- Database: MongoDB for flexible data storage
- API Layer: FastAPI for high-performance backend services
3. Development and Integration
- Implement modular AI components for different aspects of loan processing
- Create explainable AI features for transparency
- Develop real-time monitoring and alerting systems
- Build robust data validation and cleaning pipelines
4. Testing and Deployment
- Conduct parallel testing with existing systems
- Implement gradual rollout strategy
- Provide comprehensive staff training
- Establish feedback loops for continuous improvement
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Technical Architecture
Frontend Interface
- Streamlit app with a sidebar to toggle between Java and Groovy generation modes.
- Custom CSS applied for a polished, branded look.
LLM & Prompt System
- Groq API used for calling deepseek-r1-distill-llama-70b model.
- Prompts tailored to code types and dynamically injected with retrieved code context
Embedding & Storage Layer
- Liferay codebase (liferay-portal.zip) scanned for .java and .groovy files.
- Embedded using MiniLM-L6-v2 model and stored in Chroma DB for fast lookup.
RAG-Based Code Generation
- User query embedded → Top-K code snippets retrieved → Prompt injected → Groq LLM called → Output returned.
Session Management
- Session-based history management using Streamlit’s internal state and JSON-based storage.
Technical architecture:
1. AI/ML Stack
- TensorFlow for deep learning models
- scikit-learn for traditional ML algorithms
- XGBoost for gradient boosting
- SHAP for model explainability
2. Backend Framework
- FastAPI for high-performance API development
- Celery for task queue management
- Redis for caching
- MongoDB for document storage
3. Frontend Framework
- T React with TypeScript
- Redux for state management
- Material-UI for component library
- D3.js for data visualization
4. Cloud Services
- AWS ECS for containerized applications
- AWS Lambda for serverless functions
- Amazon S3 for document storage
- Amazon RDS for relational data
5. Security and Compliance
- AWS KMS for encryption
- OAuth 2.0 for authentication
- Regular security audits
- Automated compliance checking
6. Monitoring and Analytics
- ELK Stack for log management
- Prometheus for metrics
- Grafana for dashboards
- Custom analytics for model performance
Business Outcomes
Accelerated Development Workflow
- Reduced boilerplate development time by 70%.
- Enabled instant generation of Liferay-compliant modules and Groovy scripts.
Improved Developer Productivity
- Minimized learning curve for new developers by providing example-driven, context-aware suggestions.
Higher Code Quality & Consistency
- Enforced strict adherence to Liferay best practices via prompt templates.
- Reduced bugs and inconsistencies in Groovy scripts and Java modules.
Improved Collaboration & Knowledge Transfer
- Persistent multi-session chat enabled developers to resume and share past queries and solutions.
Business Outcomes:
1. Operational Efficiency
- 40% reduction in loan processing time
- 60% decrease in manual document review
- 85% automation of routine tasks
2. Risk Management
- 25% improvement in risk assessment accuracy
- 30% reduction in default rates
- Enhanced fraud detection capabilities
3. Customer Satisfaction
- 50% faster loan decisions
- 70% reduction in application errors
- Improved transparency in decision-making
4. Scalability
- 3x increase in application processing capacity
- 45% reduction in operational costs
- Improved resource utilization
5. Compliance and Reporting
- 100% audit trail coverage
- Automated compliance reporting
- Reduced regulatory risks