Build → Break → Debug → Learn → Document → Repeat
RAV AI Platform is a production-oriented AI system built to demonstrate how modern AI applications are designed, scaled, and maintained in real-world environments.
Instead of building isolated projects, this platform evolves through multiple stages—starting from basic LLM integration and progressing into a fully structured AI system with microservices, real-time processing, observability, and reliability mechanisms.
The goal of this platform is to:
- Build real-world AI systems, not just demos
- Combine backend engineering + AI capabilities
- Solve problems using LLM + system design
- Simulate production-grade AI architecture
This platform was built iteratively:
#AI API → Streaming → Smart Processing → RAG → Microservices → Kafka → Production System → Observability → Optimization
Each phase introduces new engineering challenges and solutions, mirroring how real systems evolve.
- REST API to interact with LLM
- Handles prompts, responses, and latency
- Focus: API integration, prompt engineering
- Real-time token streaming (ChatGPT-like)
- Implemented using async/reactive programming
- Upload logs → AI summarizes issues and root causes
- Uses structured prompts and parsing logic
- Focus: debugging automation using AI
- User query → vector search → context injection → LLM
- Implements semantic search using embeddings
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Multi-service system:
- auth-service
- user-service
- ai-service
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AI service supports:
- summarization
- classification
- keyword extraction
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Kafka pipeline:
#Event → AI Processor → Enriched Output
- Example: fraud detection, log enrichment
- Full architecture:
- Features:
- retries
- fallback handling
- rate limiting
- basic monitoring
These modules elevate the system to senior-level engineering standards.
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Measures:
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response quality
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hallucination rate
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consistency
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Supports prompt comparison and testing
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Tracks:
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request logs
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token usage
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latency
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cost
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Helps debug and optimize AI behavior
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Handles:
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API failures
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timeouts
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invalid outputs
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Implements:
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retries
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fallback prompts
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cached responses
- Maintains prompt history
- Enables A/B testing
- Treats prompts as versioned logic
- Reduces token usage
- Implements caching strategies
- Optimizes prompt length and responses
High-level system flow:
User Request
↓
API Gateway
↓
Auth Service
↓
AI Service
↓
Vector DB (FAISS)
↓
LLM API
↓
Response
#Async Processing: Kafka → AI Processor → Output Topic
- Java (Spring Boot)
- REST APIs
- WebClient
- LLM APIs
- Embeddings
- Prompt Engineering
- LangChain
- FAISS (Vector DB)
- PostgreSQL
- Redis (Cache)
- Apache Kafka
- Docker
- CI/CD (Jenkins)
- AI systems require engineering, not just models
- RAG is critical for real-world applications
- Observability and cost tracking are essential
- AI failures must be handled explicitly
- Backend engineering is a major advantage in AI
“AI Engineering is not about calling APIs.
It’s about building systems that can think, adapt, and scale reliably.”
- Model fine-tuning
- Custom ML pipelines
- Multi-agent systems
- Distributed AI architectures
Chandan Kumar
AI Engineer | Backend Systems | GenAI
This repository represents not just projects,
but a transition into production-grade AI engineering.