Uploaded September 2025 | Updated September 2026, 2 weeks ago
Two years of rapid iteration transformed a chat UI into a serious platform for knowledge work: first came ChatGPT as a user-facing LLM, then RAG (retrieval-augmented generation) taught us to separate data from the model by chunking, embedding, and indexing documents. From there the stack matured to make RAG production-ready. As LLMs proved capable of planning, tool-calling, and task decomposition, agentic systems emerged: models that not only answer questions but take actions, call external tools, and maintain state. The next step has been multi-agent orchestration, where specialized agents collaborate (and crucially, where humans remain in the loop) to solve more complex, long-running workflows.
#AI #LLM #RAG #RetrievalAugmentedGeneration #AIAgents #MultiAgentSystems #HumanInTheLoop #GenerativeAI #MLOps #PromptEngineering #AIInfrastructure #AIProductDesign #GPT4o
Two years of rapid iteration transformed a chat UI into a serious platform for knowledge work: first came ChatGPT as a user-facing LLM, then RAG (retrieval-augmented generation) taught us to separate data from the model by chunking, embedding, and indexing documents. From there the stack matured to make RAG production-ready. As LLMs proved capable of planning, tool-calling, and task decomposition, agentic systems emerged: models that not only answer questions but take actions, call external tools, and maintain state. The next step has been multi-agent orchestration, where specialized agents collaborate (and crucially, where humans remain in the loop) to solve more complex, long-running workflows.
#AI #LLM #RAG #RetrievalAugmentedGeneration #AIAgents #MultiAgentSystems #HumanInTheLoop #GenerativeAI #MLOps #PromptEngineering #AIInfrastructure #AIProductDesign #GPT4o
