Master retrieval-augmented generation, vector databases, embeddings, and semantic search systems.
Most teams measure RAG success with vibes, not metrics. Learn the specific evaluation approaches that reveal whether your retrieval pipeline delivers accurate, relevant results.
384, 768, 1024, or 3072 dimensions? The right choice depends on your data complexity, latency requirements, and storage budget—not the highest number available.
Compare Pinecone, Weaviate, and Qdrant for your AI project. Learn the real performance differences, pricing, and which vector database fits your specific use case.
Context loss during document chunking kills RAG accuracy. Learn the semantic chunking strategies and overlap techniques that preserve meaning while optimizing retrieval performance.
Real-world framework for choosing embedding models based on production requirements, cost, and performance—not just benchmarks. From 20+ RAG implementations.
Discover when to build an in-house data labeling team versus outsourcing. Real cost analysis, quality trade-offs, and decision frameworks from 40+ AI implementations.
Dense embeddings miss exact keywords. Sparse embeddings miss semantic meaning. Hybrid search combines both approaches to improve retrieval accuracy by 30-40% in production systems.
Vector search returning zero results? Learn the 7 most common causes—from embedding mismatches to distance thresholds—and how to fix each one quickly.
Learn how prompt compression techniques reduce AI costs by 50-80% while improving response times and maintaining accuracy. Practical implementation strategies for production systems.