jackcoder.hashnode.devUnderstand LLM ContextConcepts Fundamentals and Representation At its core, an LLM is stateless. Every API call begins with a blank slate, and the model retains nothing from previous interactions unless explicitly provided. What we call context is essentially the working ...Jan 9·11 min read
jackcoder.hashnode.devRAG and Agentic AIOverview Agentic AI (multi-agent LLM workflows) and Retrieval-Augmented Generation (RAG) are complementary. Agents run perceive → plan → act → observe loops and call tools/APIs. RAG supplies curated, evidence-backed context via an ingestion and a ret...Jan 4·16 min read
jackcoder.hashnode.devResults and errors handling strategies (in C#)Overview A consistent and robust strategy to propagate results and errors is key for both code correctness and solidity. Talking about results propagation we cannot avoid bring up functional programming. Behavior of purely functional code is predicta...Feb 11, 2024·7 min read
