PGPrasad Gaikwadinblog.prasadgaikwad.dev·12h ago · 7 min readIntegration Features: REST, Streaming, WebSocket, and a DatabaseSo far everything in this series has lived behind the command line. The model, the memory, the RAG pipeline, the agents — all real, but all reachable only through a REPL. Our checklist's next mileston00
PGPrasad Gaikwadinblog.prasadgaikwad.dev·2d ago · 6 min readPrompting Techniques with LangChain4jEverything so far in this series has relied on a single well-written prompt. But prompts are code: they deserve templates, examples, and reliable output contracts. This post covers the three prompting00
PGPrasad Gaikwadinblog.prasadgaikwad.dev·2d ago · 7 min readChains and Agents with LangChain4jSo far the demo's LLM calls have been single-shot: the chat model answers a question in one step. But many real tasks need more than that — a task may require arithmetic, a lookup in a knowledge base,00
PGPrasad Gaikwadinblog.prasadgaikwad.dev·4d ago · 6 min readDocument Processing with LangChain4jIn the last post we built a RAG pipeline that answers questions from indexed documents. But the pipeline is only as good as its input: real-world data lives in PDFs, mixed file types, and messy format00
PGPrasad Gaikwadinblog.prasadgaikwad.dev·5d ago · 6 min readRetrieval Augmented Generation (RAG) with LangChain4jIn the last few posts we gave our chatbot a memory and the ability to search documents by meaning. Now it's time to combine the two. A large language model knows a lot, but it doesn't know your docume00
