Last year, I faced a critical decision: fine-tune our LLM or implement RAG? We chose fine-tuning. It was expensive, time-consuming, and didn’t solve our core problem. After building 20+ LLM applications, I’ve learned when to use each approach. Here’s the comprehensive decision framework that will save you months of work. Figure 1: Fine-Tuning vs RAG… Continue reading
Category: AI/ML
Edge AI with ONNX Runtime: Running Models On-Device
Last year, I deployed an AI model to a mobile device. The first attempt failed—the model was too large, inference was too slow, and battery drain was unacceptable. After optimizing 15+ models for edge deployment using ONNX Runtime, I’ve learned what works. Here’s the complete guide to running AI models on-device with ONNX Runtime. Figure… Continue reading
Serverless AI Architecture: Building Scalable LLM Applications
Three years ago, I built my first serverless LLM application. It failed spectacularly. Cold starts made responses take 15 seconds. Timeouts killed long-running requests. Costs spiraled out of control. After architecting 30+ serverless AI systems, I’ve learned what works. Here’s the complete guide to building scalable serverless LLM applications. Figure 1: Serverless AI Architecture Overview… Continue reading
Deploying LLM Applications on Cloud Run: A Complete Guide
Last year, I deployed our first LLM application to Cloud Run. What should have taken hours took three days. Cold starts killed our latency. Memory limits caused crashes. Timeouts broke long-running requests. After deploying 20+ LLM applications to Cloud Run, I’ve learned what works and what doesn’t. Here’s the complete guide. Figure 1: Cloud Run… Continue reading
Cost Optimization for AI Workloads: Tracking and Reducing LLM Costs
Last quarter, our LLM costs hit $12,000. In a single month. We had no idea where the money was going. No tracking, no budgets, no alerts. That’s when I realized: cost optimization isn’t optional for AI workloads—it’s survival. Here’s how we cut costs by 65% without sacrificing quality. Figure 1: Cost Optimization Architecture The $12,000… Continue reading
Prompt Performance Monitoring: Tracking LLM Response Quality
Three weeks after launching our AI customer support system, we noticed something strange. Response quality was degrading—slowly, almost imperceptibly. Users weren’t complaining yet, but satisfaction scores were dropping. The problem? We had no way to measure prompt performance. We were optimizing blind. That’s when I built a comprehensive prompt performance monitoring system. Figure 1: Prompt… Continue reading
LLM Observability: Monitoring AI Applications in Production
Last month, our LLM application started giving wrong answers. Not occasionally—systematically. The problem? We had no visibility. No logs, no metrics, no way to understand what was happening. That incident cost us a major client and taught me that observability isn’t optional for LLM applications—it’s survival. ” alt=”LLM Observability Architecture” style=”max-width: 100%; height: auto; border-radius:… Continue reading
Advanced RAG Patterns: Beyond Basic Retrieval
Six months ago, I thought RAG was simple: retrieve chunks, send to LLM, done. Then I built a system that needed to answer questions about 50,000 technical documents. Basic retrieval failed spectacularly. That’s when I discovered advanced RAG patterns—techniques that transform RAG from a prototype into a production system. ” alt=”Advanced RAG Patterns” style=”max-width: 100%;… Continue reading
Production RAG Architecture: Building Scalable Vector Search Systems
Three months into production, our RAG system started failing at 2AM. Not gracefully—complete outages. The problem wasn’t the models or the embeddings. It was the architecture. After rebuilding it twice, here’s what I learned about building RAG systems that actually work in production. Figure 1: Production RAG Architecture Overview The Night Everything Broke It was… Continue reading
Vector Database Comparison: Pinecone vs Weaviate vs Qdrant vs Chroma – Choosing the Right One for Your RAG Application
Last March, a 3AM alert changed everything. Our Pinecone bill had tripled overnight, and I spent the next three months migrating between vector databases, learning hard lessons about what actually matters. Let me share what I discovered—and what I wish someone had told me. Figure 1: Comprehensive comparison of vector database options The Night Everything… Continue reading
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