RAG Implementation
Retrieval-Augmented Generation systems for intelligent document processing and enterprise knowledge extraction.
RAG System Cost Analysis
Token Usage Impact
❌ Basic Q&A: 600-1,000 tokens/query
Simple questions without context
⚠️ Unoptimized RAG: 2,000-5,000 tokens/query
3-6x higher costs without proper optimization
✅ Optimized RAG: 800-1,200 tokens/query
Smart chunking + model selection
Cost Optimization Techniques
- 256-token chunk optimization (vs 512+ token waste)
- 3-5 document retrieval limit (vs unlimited context)
- Smaller models with RAG context (vs premium models)
- Vector database caching (90% query reuse)
- Hybrid search combining semantic + keyword
- Progressive summarization for long documents
Technical Components
- Document ingestion and processing pipeline
- Vector database setup (Pinecone/Supabase/Chroma)
- Embedding generation and optimization
- Semantic search and retrieval engine
- Context-aware response generation
- Performance monitoring and analytics
Document Support
- PDF processing and text extraction
- Word documents and spreadsheets
- Web content and knowledge bases
- Database content and APIs
- Real-time document updates
- Multi-language content support
RAG Implementation ROI
3-5x
Higher Token Usage
Than basic Q&A systems
60-80%
Cost Reduction
With proper optimization
4-6 Weeks
Implementation
Full enterprise deployment
Net Result: RAG enables smaller, cheaper models to deliver premium-quality responses by providing relevant context, often resulting in net cost savings despite higher token usage.
Ready to Implement RAG for Your Enterprise?
Let's build an intelligent document processing system that reduces AI costs while improving accuracy.