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RAG-Based AI Code Generator for Enterprise Portal Development
The end client is a US-based enterprise software solutions organisation that uses Liferay Digital Experience Platform for developing and maintaining enterprise portals.
The organisation required an AI-assisted development tool that could understand Liferay architecture, retrieve relevant platform context, and generate review-ready code for Liferay DXP 7.4.
The end client’s identity is withheld due to contractual confidentiality and NDA obligations.
Project Overview
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- The project involved the development of a retrieval-augmented AI coding assistant for Liferay DXP 7.4 development teams.
The solution was designed and developed by a SilwaTech partner company through SilwaTech’s partner delivery ecosystem.
The platform covered
- Java module generation
- Groovy script generation
- Semantic retrieval from the Liferay source codebase
- Structured prompt templates based on Liferay conventions
- An interactive developer workspace
- Persistent conversation and session history
- Secure management of model credentials
- Traceable AI-generated outputs
The solution combined source-code retrieval, prompt engineering and conversational development tools to produce code aligned with relevant Liferay patterns.
Generated outputs were intended for developer review and testing before use in production environments.
- The project involved the development of a retrieval-augmented AI coding assistant for Liferay DXP 7.4 development teams.
Case Study Topics
Business Challenge
- Liferay development teams encountered several productivity and consistency challenges.
Key challenges included
- Repetitive creation of boilerplate for modules and services
- Manual preparation of custom Java and Groovy scripts
- General-purpose AI tools lacking knowledge of Liferay APIs and architecture
- Difficulty maintaining alignment with Liferay DXP 7.4 conventions
- Inconsistent code quality across development teams
- A steep learning curve for developers new to Liferay
- Time spent searching platform documentation and source code
- Risk of generated code using outdated APIs or unsuitable patterns
The client needed a domain-aware AI assistant capable of generating contextual Liferay code while keeping developers responsible for validation, testing and final implementation.
Solution Architecture
Solution Delivered
Java and Groovy Code Generation
A conversational development interface supported the primary code-generation requirements of Liferay teams.
Key capabilities included
- Generation of Java-based Liferay modules
- Generation of Groovy scripts
- Switching between Java and Groovy modes
- Context-aware code suggestions
- Generation of common module structures
- Support for services and configuration patterns
- Output structured around Liferay DXP 7.4 conventions
- Developer review before implementation
RAG Pipeline over the Liferay Codebase
A retrieval-augmented generation pipeline grounded responses in relevant Liferay source-code context.
The pipeline provided
- Indexing of the Liferay DXP 7.4 source codebase
- Semantic representation using Sentence Transformer embeddings
- Storage of embeddings within Chroma DB
- Retrieval of relevant classes, methods and code examples
- Injection of selected code snippets into model prompts
- Context-aware code generation
- Reduced reliance on the model’s general knowledge alone
- Source context to support developer verification
Prompt Engineering Framework
A modular prompt framework guided the model towards appropriate platform structures and coding conventions.
Capabilities included
- Prompt templates incorporating Liferay coding standards
- Language-specific instructions for Java and Groovy
- Context windows containing retrieved source-code examples
- Structured output requirements
- Reusable and maintainable prompt files
- Extensibility for additional code types
- Guardrails against unsupported or irrelevant output
Developer Workspace
An interactive workspace provided developers with a practical environment for generating and reviewing code.
The solution included
- Streamlit-based conversational interface
- Custom interface styling
- Light and dark display options
- Persistent conversation history
- Support for multiple working sessions
- Easier handover of previous development context
- FastAPI-based backend services
- Retry and fault-tolerance logic
- Traceable records of prompts and generated outputs
Technology Stack
The technology environment included
- Sentence Transformer embeddings for semantic indexing
- Chroma DB for vector storage and code retrieval
- Groq API for large language model access and orchestration
- Streamlit for the developer interface
- FastAPI for backend services
- JSON and Chroma DB for session and embedding storage
- OAuth 2.0 for authentication
- Encrypted handling of API credentials
- Cloud infrastructure for scalable deployment
- Java and Groovy as generated code formats
- Liferay DXP 7.4 as the target development platform
Engagement Model
The project was delivered through SilwaTech’s partner ecosystem under a partner-led implementation model.
The solution architecture, retrieval pipeline, AI development, application development, testing and technical implementation were performed by an experienced SilwaTech partner company. SilwaTech contributed through engagement alignment, partner collaboration and coordination of the required generative AI and Liferay-related capabilities.
The cross-functional delivery team included specialists in
- Generative AI engineering
- Retrieval-augmented generation
- Semantic code search
- Liferay development
- Backend and frontend engineering
- User-experience design
- Cloud deployment
- Quality assurance and security testing
Delivery followed an iterative approach, beginning with source-code indexing and retrieval before progressing to code generation, prompt refinement, developer-interface development and secure deployment.
The identities of the end client and implementation partner remain confidential. SilwaTech does not represent itself as the direct contracting party or sole technical implementer for this engagement.
Security and Governance Focus
The assistant processed proprietary development requests, retrieved source-code context and used external model APIs.
Key areas of focus included
- OAuth 2.0-based authentication
- Role-based access to the development assistant
- Encrypted storage and use of API credentials
- Secure management of authentication tokens
- Controlled access to indexed code
- Persistent session history for traceability
- Logging of prompts and generated outputs
- Prompt templates aligned with defined coding standards
- Developer review and testing of generated code
- Controlled deployment into enterprise development environments
The platform was designed to assist developers rather than autonomously deploy generated code. All outputs required appropriate code review, security testing and quality assurance before production use.
Value Delivered
The engagement contributed to
- Faster generation of common Liferay code structures
- Reduced time spent preparing repetitive boilerplate
- Quicker access to relevant Liferay source-code context
- Improved consistency of generated Java and Groovy code
- Faster onboarding for developers new to Liferay
- Better alignment with Liferay DXP 7.4 patterns
- Easier collaboration through persistent session history
- Reduced dependence on general-purpose AI outputs
- Traceable and reviewable code-generation activity
- A modular foundation for supporting additional code types and platforms
Why Silwatech
This engagement demonstrates the type of domain-aware generative AI capability SilwaTech can support through its technology partner ecosystem, including
Liferay DXP and enterprise portal knowledge
Access to specialists in retrieval-augmented generation
Semantic retrieval across large source-code repositories
Prompt engineering for platform-specific development
Secure and traceable developer-assistance workflows
Human review and testing controls for generated code
Extensible AI architectures supporting additional technologies
Structured collaboration with specialised implementation partners
Selected Enterprise & Public Sector Experience
Our project experience spans government authorities, regulated enterprises, and major organisations across multiple industries.
References shown may include direct SilwaTech engagements and work delivered through our contracted partner network. Inclusion does not imply a direct contractual relationship with, or endorsement of, SilwaTech.
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