RAG Architecture Review
Find What's Broken Before Production
RAG systems often hallucinate, return stale answers, or don't scale. Demo performance rarely matches production reality. Use this framework to find the hidden failure points in your stack.
The Problem
RAG systems often hallucinate, return stale answers, or don't scale
Demo performance ≠ production performance
Complex stacks hide the real failure points
Teams lack the expertise to diagnose retrieval vs. generation issues
Architecture Efficiency Check
We often find RAG systems slowed down by "Agentic Bloat"-using complex agents where simple code works better.
Inefficient (Agentic)
- User asks question
- LLM parses query
- LLM calls "search_tool"
- System runs search
- LLM reads results & answers
3 LLM Calls • 3.5s Latency
Optimized (Deterministic)
- User asks question
- Code runs search (0ms decision)
- Running search...
- LLM reads results & answers
1 LLM Call • 1.2s Latency
If a tool must always be called (like searching your knowledge base), using an "agent" to decide that is just adding latency for no reason.
What We Review
Chunking strategy and document preprocessing
Embedding model selection and alignment
Retrieval pipeline (single vs. multi-stage, rerankers)
Vector database configuration and update strategy
Prompt design and context injection
Evaluation and monitoring setup
Common Issues We Find
No smart context retrieval strategy - increasing token counts, latency, and impacting accuracy
LLM components with too many responsibilities (e.g., a single prompt trying to reason, format, and filter)
MCP servers used where simple APIs would suffice, adding unnecessary latency and infrastructure cost
Lack of evaluation framework or an architecture that makes granular evaluation impossible
Wasting tokens on LLMs rewriting structured data (like image URLs) that should be handled programmatically
The Review Process
Deep-dive into chunking and embedding strategies
Benchmark retrieval quality against a 'Golden Set'
Analyze component-by-component latency and cost
Identify and prioritize root causes of hallucinations
Your Optimization Plan
A thorough architecture review should result in a prioritized remediation plan. You'll identify exactly what's broken, why it's broken, and the fastest path to fix it-ranked by impact and effort.
Build Your RAG Evaluation Strategy
Stop guessing why your RAG system underperforms. Use our Evaluation Builder to create a systematic assessment for your specific use case.