Insights & Updates
Explore insights from our engineering team on software development, AI, cloud architecture, and industry best practices.
Master Claude API integration for enterprise applications. Learn authentication, streaming responses, tool use, vision capabilities, and best practices for building reliable AI-powered features with Anthropic's most advanced models.
Jishu Labs
CTO
January 20, 2026
13 min read
Featured Article
9 articles tagged “rag”
Clear tag filterYou usually do not need a dedicated vector database. A practical pgvector schema covering chunk modelling, tenant filtering before search, index choice, and the re-embedding problem nobody plans for.
Long context windows and better tool use took work away from retrieval-augmented generation. RAG did not become obsolete — its job got narrower. A decision framework for when to retrieve, when to load, and when to call a tool.
An embedding is a list of numbers representing meaning, so that similar things sit close together. What they are, how similarity search uses them, and the practical decisions — dimensions, chunking, distance metric — that determine whether retrieval works.
Prompt wording stopped being the bottleneck around 2025. The harder problem — deciding what lands in the context window at every turn — got a name. What context engineering is, and the four things it covers.
Three ways to make a model behave the way you need, routinely treated as competitors when they solve different problems. What each actually changes, and the order to try them in.
Master LangChain for building production-ready AI applications. Learn chains, agents, memory systems, RAG pipelines, and deployment strategies with practical code examples for real-world use cases.
Choose the right vector database for your AI applications. Compare Pinecone, Weaviate, Qdrant, and Milvus on performance, scalability, features, and cost for production RAG and semantic search systems.
Master Retrieval Augmented Generation (RAG) for building accurate, grounded AI applications. Learn architecture patterns, vector databases, chunking strategies, and production deployment best practices.
Master Retrieval Augmented Generation (RAG) for enterprise AI applications. Learn advanced chunking strategies, vector databases, hybrid search, and evaluation techniques to build accurate, reliable AI systems grounded in your data.
Get the latest insights on software development, AI, and technology trends delivered to your inbox.
Get consistent results. Collaborate in real-time.
Build Intelligent Apps. Work with Jishu Labs.