by wshobson
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
by wshobson
Implement robust evaluation workflows for LLM applications using automated metrics, human feedback, and benchmarking. Ideal for teams testing LLM performance, comparing models, or validating AI improvements.
by wshobson
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
by wshobson
Optimize vector index performance for latency, recall, and memory. Ideal for tuning HNSW parameters, choosing quantization strategies, and scaling vector search infrastructure in AI and backend applications.
