AGI Learning Framework and Practical AI Application Guide

After more than a year without publishing, the author resumes sharing insights on digital tools and intelligent applications, announcing a series of AGI summaries and practical implementations that document a year of research and future plans.

AGI Learning Framework

AGI (Artificial General Intelligence) is divided into five levels; learners are advised to focus on the first two levels initially, reviewing survey papers before diving into specific books.

Level 1: Chatbot – Core abilities: knowledge and language. Study NLP through survey papers covering sub‑fields such as machine translation, code generation, reading comprehension, and dialogue systems. Recommended books: Practical Natural Language Processing and Introduction to Natural Language Processing .

Level 2: Reasoner – Core ability: reasoning (both natural‑language and formal). Review reasoning survey papers and the book Introduction to Logic to understand the boundaries of large‑model reasoning.

Level 3: Agent – Core abilities: domain expertise and planning. Study LLM‑Agent survey papers; recommended book: Artificial Intelligence: A Modern Approach (4th Edition) .

Level 4: Innovator – Core ability: creativity, built upon strong domain expertise, enabling AI to assist humans in scientific discovery, artistic creation, or engineering design.

Level 5: Organizer – Core ability: leadership, coordinating the previous four roles to autonomously manage cross‑business processes, resources, and feedback loops, approaching human‑level organizational capability.

Learning Through Practice – Defining Goals

Short‑term: Use large‑model generation to produce front‑end or back‑end code, aiming to double development efficiency.

Mid‑term: Master model fine‑tuning or post‑training to improve code‑generation accuracy by 30%.

Long‑term: Build a consumer‑ or enterprise‑facing intelligent application that boosts business growth by 30%.

How to Find the Fit Between Technology and Scenario?

Application scenarios fall into two categories: internal intelligent assistants for employees and external intelligent assistants for users. Both should start from user pain points. For example, internal assistants can streamline AB‑testing workflows, reducing experiment cost and accelerating strategy reporting.

Final Thoughts – Shifts Needed for Engineers Under AI Infrastructure

With models like DeepSeek lowering development costs, many high‑valuation applications are expected to emerge within a year. The biggest challenge for engineers is identifying viable scenarios and business models, a process the author describes as “crossing the river by feeling the stones.”

To stay competitive and build new capabilities, engineers should:

Look outward for inspiration from leading AI products (e.g., Doubao).

Continuously monitor advances in large‑model capabilities and post‑training techniques.

Actively develop their own large‑model applications, as hands‑on projects yield deeper understanding than passive reading.

If you have an intelligent‑automation idea, try building it tonight.