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LLM & RAG Architecture Enhancement Service

Status: βœ… Phase 1 Complete - Production Ready!
Last Updated: October 9, 2025
Effort: Phase 1 (4 weeks) βœ… Complete | Phase 2-3 (16 weeks) πŸ”œ Planned


πŸ“– Overview​

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

🎯 Business Value​

Phase 1 Impact (4 weeks)​

  • πŸ’° Cost Reduction: 50-70%
  • ⚑ Latency Improvement: 40-50%
  • πŸ”§ Provider Flexibility: 3 LLM providers with automatic fallback

Full Implementation (20 weeks)​

  • πŸ“Š Quality Improvement: 20-30% (via ColBERT + DSPy)
  • πŸ†• New Capabilities: Graph RAG, multimodal support
  • πŸ’΅ ROI: 2-3x in first year

πŸ“š Documentation Structure​

1. Overview - Start Here!​

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 Makers​

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 Dive​

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 Selection​

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 Code​

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

πŸš€ Quick Start​

For Stakeholders (15 minutes)​

  1. Read Executive Summary
  2. Review business case and expected ROI
  3. Make go/no-go decision on Phase 1

For Engineering Managers (1 hour)​

  1. Read Executive Summary
  2. Skim Architecture Plan - focus on roadmap
  3. Assess team capacity and allocate resources

For Engineers (3-4 hours)​

  1. Read Executive Summary - understand "why"
  2. Read Library Comparison - understand choices
  3. Follow Implementation Guide step-by-step

For Architects (4-5 hours)​

  1. Read all documents thoroughly
  2. Validate technical approach
  3. Review integration points
  4. Provide feedback and approval

πŸ“Š Enhancement Priorities​

πŸ”΄ Priority 1: Quick Wins (Weeks 1-4)​

EnhancementImpactEffortTimeline
Multi-LLM SupportCost -20-30%Medium2 weeks
Prompt CompressionCost -40-60%Low1 week
Enhanced CachingCost -30-40%, Latency -40%Low1 week
Query RoutingLatency -20%Low1 week

Total: 50-70% cost reduction, 40-50% latency improvement


🟑 Priority 2: Quality Improvements (Weeks 5-8)​

EnhancementImpactEffortTimeline
ColBERT RerankingQuality +15-20%Medium2-3 weeks
DSPy PromptsQuality +15-25%High3-4 weeks

Total: 20-30% quality improvement


πŸ”΅ Priority 3: Advanced Features (Weeks 9-20)​

EnhancementImpactTimeline
Graph RAGNew capabilities4-6 weeks
Multimodal RAGImage/audio support4-6 weeks
Model Fine-TuningLong-term optimization6-8 weeks

Total: New capabilities for specific use cases


πŸŽ“ What You Already Have​

Your current system has excellent foundation:

βœ… Production-Ready RAG

  • Hybrid retrieval (BM25 + embeddings + reranking)
  • 5 vector stores (OpenSearch, MongoDB, Qdrant, Azure, Vertex)
  • Document processing pipeline
  • Multiple chunking strategies

βœ… Enterprise Features

  • Multi-tier rate limiting
  • Cost tracking and budget enforcement
  • Safety guardrails (NeMo)
  • Comprehensive observability (LangSmith, Prometheus, Jaeger)

βœ… Quality Assurance

  • RAGAS evaluation framework
  • A/B testing infrastructure
  • Online monitoring
  • Custom evaluators

βœ… Advanced Capabilities

  • Faceted search
  • Query understanding & expansion
  • Deduplication
  • Source attribution
  • Document summarization
  • Semantic caching

πŸ†• What We're Adding​

Phase 1: Foundation (4 weeks)​

  • πŸ”„ Multi-LLM Support - OpenAI + Anthropic + Google
  • πŸ—œοΈ Prompt Compression - LLMLingua (40-60% cost savings)
  • πŸ’Ύ Enhanced Caching - GPTCache integration
  • πŸ”€ Query Routing - Adaptive retrieval strategies

Phase 2: Quality (4 weeks)​

  • 🎯 ColBERT Reranking - State-of-the-art retrieval
  • πŸ› οΈ DSPy Prompts - Automatic optimization

Phase 3: Advanced (12 weeks)​

  • πŸ•ΈοΈ Graph RAG - Knowledge graphs with Neo4j
  • 🎨 Multimodal - Image/audio with CLIP & Whisper
  • πŸŽ“ Fine-Tuning - Domain-specific models with PEFT

πŸ“ˆ Expected Results​

After Phase 1 (4 weeks)​

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)​

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)​

Capability              Status
================================================================
Graph RAG βœ… Contract intelligence, compliance
Multimodal RAG βœ… Medical imaging, manufacturing QC
Fine-Tuned Models βœ… Domain embeddings, custom rerankers
Long-term Cost -60% to -80%

πŸ’° Business Case​

Investment​

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

Returns​

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


βœ… Next Steps​

This Week (Planning)​

  1. Read Overview for navigation guide
  2. Review Executive Summary
  3. Skim Architecture Plan
  4. Discuss with team and stakeholders
  5. Decide on Phase 1 approval

Upon Approval (Week 1)​

  1. Set up development environment
  2. Install Phase 1 dependencies
  3. Follow Implementation Guide
  4. Start with Multi-LLM support
  5. Test and benchmark each component

Weeks 2-4 (Implementation)​

  • Week 2: Prompt compression
  • Week 3: Enhanced caching
  • Week 4: Query routing + testing

πŸ“ž Support & Questions​

Finding Specific Information​

"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


Current System Documentation​

Other Services​

Examples​


🎯 Success Metrics​

Phase 1 KPIs (Track Weekly)​

  • 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

πŸ“ Version History​

VersionDateChangesStatus
1.0Oct 9, 2025Initial planning documents createdβœ… Complete

Ready to get started? Begin with the Overview or jump straight to the Executive Summary for business context.