TobyDev ຮຽນສ້າງໂປຣແກຣມບໍ່ຮູ້ຫຍັງ

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This is my way

🤖 RAG gives LLMs access to your data. Agentic RAG gives them judgment about what to do with it.

The difference shows up in how each system handles uncertainty. Standard retrieval-augmented generation runs a fixed pipeline: encode the query, search the vector database, retrieve similar documents, generate a response. It works well when the query is well-formed and the right context already exists in one place.

Agentic RAG adds a layer of reasoning at almost every step — rewriting the query before retrieval, deciding whether the initial results are actually sufficient, choosing between multiple source types (vector DB, APIs, live web), and evaluating whether the final answer is relevant before surfacing it. If any of those checks fail, it loops back rather than proceeding.

That feedback loop is what makes it genuinely different, not just incrementally better. A standard RAG pipeline doesn't know when it's about to give a bad answer. An agentic one can at least ask the question.

If you want to understand how to design systems that reason through retrieval rather than just execute it, we cover this in depth at our Agentic AI Bootcamp starting May 5th, 2026 → https://hubs.la/Q04d5h720

ສະນັ້ນ gemini ກໍ່ແມ່ນ Claude code ຂຽນ

Google CEO Sundar Pichai announced that 75% of all new code at the company is now AI-generated and approved by engineers, up from 50% last fall and over 30% a year ago.

Human engineers review, modify, and accept the output rather than writing from scratch, shifting roles toward orchestration in "agentic workflows".

A recent complex code migration finished six times faster using AI agents and engineers. This rapid adoption highlights AI's transformation of software development at Google, with humans as final gatekeepers.

Follow for more updates.