Case Studies
Real-world AI implementations with honest reflections on what worked, what didn't, and what I learned
Building a Production RAG System That Actually Scales
How we went from 5-second response times to 300ms while handling 10x traffic
The Challenge
A Fortune 500 client needed to transform their document search system handling 50M+ documents into an intelligent Q&A system. The existing elasticsearch setup took 5+ seconds per query and couldn't handle natural language questions.
What We Tried (And Failed)
❌ Attempt #1: Direct GPT-4 Integration
What we did: Naive implementation passing entire documents to GPT-4
Why it failed: $10,000/day in API costs, 30-second timeouts
Lesson learned: Context window limits and costs compound quickly at scale
❌ Attempt #2: Simple Vector Database
What we did: Basic ChromaDB setup with OpenAI embeddings
Why it failed: Poor relevance, no handling of document updates
Lesson learned: Embeddings alone miss crucial keyword matching
The Breakthrough
After two failed attempts and honest conversations with the team, we realized we needed a hybrid approach. The junior engineer on our team suggested combining BM25 keyword search with semantic search - a insight that became the foundation of our solution.
✅ Final Architecture
- Hybrid search: BM25 + Dense retrieval with cross-encoder reranking
- Hierarchical indexing: Document → Section → Paragraph
- Smart caching: Redis for embeddings, edge caching for common queries
- Async processing: Background jobs for document processing
- Fallback strategies: Graceful degradation when models are unavailable
Results & Long-term Impact
What I'd Do Differently
Looking back, I should have started with the hybrid approach. My bias toward "latest and greatest" led me to overlook proven information retrieval techniques. The team's collaborative approach and willingness to admit failures quickly was what saved the project.
More Case Studies
When Real-Time AI Wasn't The Answer
How I convinced a client NOT to use AI for their trading system, saving them $2M and our relationship.
Mentoring Junior Devs in AI Development
How teaching two bootcamp grads to build LLM apps taught me more about clear architecture than any senior role.
3-Year Evolution of an AI Platform
From MVP to 50M users: How continuous iteration beat big-bang releases in production AI.
Thought Leadership
When NOT to Use AI: A Guide for Technical Leaders
After implementing AI solutions for 5+ years, I've learned that knowing when NOT to use AI is just as valuable as knowing how to implement it. Here's my framework for making that decision...
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