LLM Application Development skills covering advanced prompt engineering, embeddings, hybrid search, LangChain architecture, evaluation frameworks, RAG implementation, similarity search patterns, and vector index optimization for production AI systems.
Master advanced prompt engineering techniques to maximize LLM performance and reliability
Select and optimize embedding models for semantic search and RAG applications
Combine vector and keyword search for improved retrieval in RAG systems
Design LLM applications using LangChain framework with agents, memory, and tools
Implement comprehensive evaluation strategies for LLM applications with automated metrics
Build Retrieval-Augmented Generation systems with vector databases and semantic search
Implement efficient similarity search with vector databases and optimization patterns
Optimize vector index performance for latency, recall, and memory efficiency