LLM & RAG Architecture Enhancement Service
Status: β Phase 1 Complete - Production Ready!
Last Updated: October 9, 2025
Effort: Phase 1 (4 weeks) β Complete | Phase 2-3 (16 weeks) π Planned
π Overviewβ
This service provides comprehensive planning and implementation guidance for enhancing the existing LLM & RAG system with:
- Multi-LLM provider support (OpenAI, Anthropic, Google)
- Prompt compression for 40-60% cost reduction
- Advanced reranking with ColBERT for 15-20% quality improvement
- Systematic prompt engineering with DSPy
- Enhanced semantic caching with GPTCache
- Query routing for adaptive processing
π― Business Valueβ
Phase 1 Impact (4 weeks)β
- π° Cost Reduction: 50-70%
- β‘ Latency Improvement: 40-50%
- π§ Provider Flexibility: 3 LLM providers with automatic fallback
Full Implementation (20 weeks)β
- π Quality Improvement: 20-30% (via ColBERT + DSPy)
- π New Capabilities: Graph RAG, multimodal support
- π΅ ROI: 2-3x in first year
π Documentation Structureβ
1. Overview - Start Here!β
Quick navigation guide to all planning documents. Read this first to understand the documentation structure.
Who: Everyone
Read Time: 5 minutes
2. Executive Summary - For Decision Makersβ
Business case, ROI analysis, and recommendations for stakeholders and decision-makers.
Who: Stakeholders, managers, executives
Read Time: 15 minutes
Contents:
- Current state assessment
- Expected impact and ROI
- Decision framework
- Action plan
3. Architecture Plan - Technical Deep Diveβ
Comprehensive technical analysis of current architecture and enhancement roadmap.
Who: Engineers, architects, technical leads
Read Time: 2-3 hours
Contents:
- Current architecture inventory (20 pages)
- Gap analysis (10 pages)
- Enhancement recommendations (15 pages)
- 5-phase implementation roadmap (5 pages)
4. Library Comparison - Library Selectionβ
Detailed evaluation of 20+ open-source libraries with recommendations.
Who: Engineering team, technical decision-makers
Read Time: 1-2 hours
Contents:
- Comparison matrices for all library categories
- ROI analysis per library
- Detailed technical analysis
- Implementation checklists
5. Implementation Guide - Ready-to-Use Codeβ
Copy-paste ready code examples for implementing each enhancement.
Who: Engineers implementing the enhancements
Read Time: 3-4 hours (or reference as needed)
Contents:
- Multi-LLM support integration (with code)
- Prompt compression setup (with code)
- Enhanced caching configuration (with code)
- Query routing implementation (with code)
- ColBERT reranking (with code)
- Benchmarking scripts
π Quick Startβ
For Stakeholders (15 minutes)β
- Read Executive Summary
- Review business case and expected ROI
- Make go/no-go decision on Phase 1
For Engineering Managers (1 hour)β
- Read Executive Summary
- Skim Architecture Plan - focus on roadmap
- Assess team capacity and allocate resources
For Engineers (3-4 hours)β
- Read Executive Summary - understand "why"
- Read Library Comparison - understand choices
- Follow Implementation Guide step-by-step
For Architects (4-5 hours)β
- Read all documents thoroughly
- Validate technical approach
- Review integration points
- Provide feedback and approval
π Enhancement Prioritiesβ
π΄ Priority 1: Quick Wins (Weeks 1-4)β
Enhancement | Impact | Effort | Timeline |
---|---|---|---|
Multi-LLM Support | Cost -20-30% | Medium | 2 weeks |
Prompt Compression | Cost -40-60% | Low | 1 week |
Enhanced Caching | Cost -30-40%, Latency -40% | Low | 1 week |
Query Routing | Latency -20% | Low | 1 week |
Total: 50-70% cost reduction, 40-50% latency improvement
π‘ Priority 2: Quality Improvements (Weeks 5-8)β
Enhancement | Impact | Effort | Timeline |
---|---|---|---|
ColBERT Reranking | Quality +15-20% | Medium | 2-3 weeks |
DSPy Prompts | Quality +15-25% | High | 3-4 weeks |
Total: 20-30% quality improvement
π΅ Priority 3: Advanced Features (Weeks 9-20)β
Enhancement | Impact | Timeline |
---|---|---|
Graph RAG | New capabilities | 4-6 weeks |
Multimodal RAG | Image/audio support | 4-6 weeks |
Model Fine-Tuning | Long-term optimization | 6-8 weeks |
Total: New capabilities for specific use cases
π What You Already Haveβ
Your current system has excellent foundation:
β Production-Ready RAG
- Hybrid retrieval (BM25 + embeddings + reranking)
- 5 vector stores (OpenSearch, MongoDB, Qdrant, Azure, Vertex)
- Document processing pipeline
- Multiple chunking strategies
β Enterprise Features
- Multi-tier rate limiting
- Cost tracking and budget enforcement
- Safety guardrails (NeMo)
- Comprehensive observability (LangSmith, Prometheus, Jaeger)
β Quality Assurance
- RAGAS evaluation framework
- A/B testing infrastructure
- Online monitoring
- Custom evaluators
β Advanced Capabilities
- Faceted search
- Query understanding & expansion
- Deduplication
- Source attribution
- Document summarization
- Semantic caching
π What We're Addingβ
Phase 1: Foundation (4 weeks)β
- π Multi-LLM Support - OpenAI + Anthropic + Google
- ποΈ Prompt Compression - LLMLingua (40-60% cost savings)
- πΎ Enhanced Caching - GPTCache integration
- π Query Routing - Adaptive retrieval strategies
Phase 2: Quality (4 weeks)β
- π― ColBERT Reranking - State-of-the-art retrieval
- π οΈ DSPy Prompts - Automatic optimization
Phase 3: Advanced (12 weeks)β
- πΈοΈ Graph RAG - Knowledge graphs with Neo4j
- π¨ Multimodal - Image/audio with CLIP & Whisper
- π Fine-Tuning - Domain-specific models with PEFT
π Expected Resultsβ
After Phase 1 (4 weeks)β
Metric Baseline Target Improvement
================================================================
Cost per Query $0.10 $0.03-0.05 -50% to -70%
P95 Latency 3000ms 1500ms -50%
Cache Hit Rate 25% 50-60% +100% to +140%
LLM Providers 1 (OpenAI) 3 +200%
After Phase 2 (8 weeks total)β
Metric Baseline Target Improvement
================================================================
NDCG@5 0.75 0.85-0.90 +13% to +20%
Faithfulness 0.80 0.90-0.95 +13% to +19%
Answer Quality 0.85 0.95 +12%
After Phase 3 (20 weeks total)β
Capability Status
================================================================
Graph RAG β
Contract intelligence, compliance
Multimodal RAG β
Medical imaging, manufacturing QC
Fine-Tuned Models β
Domain embeddings, custom rerankers
Long-term Cost -60% to -80%
π° Business Caseβ
Investmentβ
Phase 1 (Immediate):
- Engineering: 1 senior engineer Γ 4 weeks = 160 hours
- Infrastructure: Minimal (Redis already in place)
- External Services: LLM provider accounts (Anthropic, Google)
- Total Cost: ~$20-30K
Returnsβ
Annual Savings:
- LLM Costs: $100K baseline β $30-50K = $50-70K/year
- Infrastructure: Reduced caching costs = $10-20K/year
- Total Annual Savings: $60-90K/year
ROI: 2-3x in first year
β Next Stepsβ
This Week (Planning)β
- Read Overview for navigation guide
- Review Executive Summary
- Skim Architecture Plan
- Discuss with team and stakeholders
- Decide on Phase 1 approval
Upon Approval (Week 1)β
- Set up development environment
- Install Phase 1 dependencies
- Follow Implementation Guide
- Start with Multi-LLM support
- Test and benchmark each component
Weeks 2-4 (Implementation)β
- Week 2: Prompt compression
- Week 3: Enhanced caching
- Week 4: Query routing + testing
π Support & Questionsβ
Finding Specific Informationβ
"How much will this cost?"
β See Executive Summary - Business Case section
"What libraries should we use?"
β See Library Comparison - Recommended Stack
"How do I implement X?"
β See Implementation Guide - Ready-to-use code
"What's the current architecture?"
β See Architecture Plan - Architecture Inventory
"What are the risks?"
β See Executive Summary - Risk Mitigation
π Related Documentationβ
Current System Documentationβ
Other Servicesβ
Examplesβ
π― Success Metricsβ
Phase 1 KPIs (Track Weekly)β
- Cost per query: Target <$0.05 (baseline: $0.10)
- P95 latency: Target <2000ms (baseline: 3000ms)
- Cache hit rate: Target >50% (baseline: 25%)
- Provider uptime: >99.9%
- Quality maintained: RAGAS faithfulness >0.80
π Version Historyβ
Version | Date | Changes | Status |
---|---|---|---|
1.0 | Oct 9, 2025 | Initial planning documents created | β Complete |
Ready to get started? Begin with the Overview or jump straight to the Executive Summary for business context.