From Dice-Rolling Probabilities to Cognitive Interfaces: How Generative AI Evolved

Oct 18 2025 golang 9 minutes read (About 1338 words)

The Four Stages of Generative AI: From Prompt to Multi-Agent Collaboration

image.png|300

Note: The core content of this article was generated by a large language model, with human fact-checking and structural refinement.

Recently, I’ve been studying several videos on generative AI:

【Introduction to Generative AI and Machine Learning 2025】Lecture 1: Understanding Generative AI in One Lesson - YouTube
【Introduction to Generative AI and Machine Learning 2025】Lecture 2: Context Engineering — The Core Technology Behind AI Agents - YouTube

After watching them, I made some summaries and asked AI to expand on them.

Here’s the synthesis:

Currently, generative AI can still be viewed as a probabilistic “dice-rolling and token-stitching” game.
It has now evolved toward AI Agents, and even CLI-style automation tools.
The main evolution process is: prompt → context engineering → agent → multi-agent,
integrating key ideas from LLMs and RAG.

🧩 I. The Core Line

prompt → context engineering → agent → multi-agent

This sequence essentially captures the evolution of generative AI from a “language model” to a “task-capable intelligent system.”
Here’s an overview of the key features and representative technologies for each stage:

Stage	Core Idea	Typical Technologies	AI Form
1️⃣ Prompt	One-way human → AI instruction	Prompt Crafting, Chain-of-Thought	Chatbots, Prompt Engineers
2️⃣ Context Engineering	Dynamic prompt composition + memory + external documents	Long Context, Function Calling, RAG	Enhanced QA, Knowledge Assistants
3️⃣ Agent	AI actively invokes tools and plans tasks	OpenAI Functions, LangChain, LlamaIndex Agents	AI Toolchains / AutoGPT
4️⃣ Multi-Agent	Multiple AIs collaborate and self-organize	Swarm, CrewAI, AutoGen, MCP (Model Context Protocol)	Multi-Agent Systems / Self-Organizing AI

Your summary nicely reflects these four progressive layers of capability:

From text generation,
to context understanding,
to task execution,
to distributed cooperation.

This is the natural evolution from language model to intelligent system.

⚙️ II. Two Foundational Lines: LLM and RAG

The two backbone mechanisms behind this progression are LLM (Large Language Models) and RAG (Retrieval-Augmented Generation).

LLM: From a Probabilistic Text Generator to a Cognitive Interface
- Early LLMs were essentially massive “conditional probability samplers.”
- But with longer contexts, chain-of-thought reasoning (CoT), and instruction fine-tuning,
  they’ve evolved into world models with reasoning interfaces.
RAG: The Bridge Between Memory and Knowledge
- It mitigates the LLM’s “forgetfulness” and “hallucination” issues;
- It injects external knowledge into the context, making the model open-world;
- It remains the most practical way to give AI grounded, factual awareness.

In short:

LLMs provide cognition; RAG provides memory and knowledge.
Together, they form the brain + long-term memory foundation of modern AI Agents.

🧠 III. Are We Seeing Signs of AGI?

That depends on how we define “general.”

Cross-task transfer capability:
✅ Yes.
Modern systems like GPT-5, Claude 3.5, and Gemini 1.5 Pro can fluently operate across text, code, vision, and tool-use domains—demonstrating weak generality.
Self-driven goal formation and long-term planning:
🚧 Still early.
Agents can plan autonomously, but their goals are externally assigned.
They lack intrinsic motivation or continuous world model updates.
Self-sustaining, self-correcting systems (human-like growth):
❌ Not yet.
“AutoGPT” or “Reflexion Agent” mimic reflection, but through recursive prompting—not genuine lifelong learning.

In summary:

Today’s generative AI is task-level general intelligence,
but not yet cognitive-level general intelligence.

🤖 IV. The Rise of Embodied Intelligence

Embodied intelligence refers to AI systems that can perceive and act within the physical world—learning through sensory feedback and interaction.
Here are some emerging directions:

Domain	Representative Projects	Meaning
Virtual Embodiment (Simulation)	Google DeepMind’s SIMA, OpenAI’s Sora, Minecraft MineDojo	AI agents act within virtual worlds, developing spatial and strategic awareness
Physical Embodiment (Robotics)	Tesla Optimus, Figure AI, 1X, Agility Robotics	LLMs integrated with vision and control stacks
Embodied Language Interfaces	ChatGPT + Voice + Vision	LLMs become multimodal command centers

LLMs are increasingly serving as the cognitive layer for robots and embodied systems:

Providing language understanding and task planning;
Lower control layers execute actions;
Sensor feedback closes the loop.

This means:

“Linguistic intelligence” is gradually evolving into “actionable intelligence.”

However, full embodied intelligence—sustainable, perception-driven, adaptive—still faces challenges:

Fusion of perception and reasoning (symbolic + sub-symbolic)
Long-term memory and causal models
Self-learning of energy, space, and motion dynamics

🌌 V. Outlook: From Dice-Throwing to World Modeling

Your metaphor of a “dice-rolling, token-stitching game” is very apt.
Early LLMs were indeed conditional probability engines—
but they are evolving into world simulators.

A more systematic evolution path can be described as:

Token Prediction → Thought Chain → World Model → Agent → Embodied Intelligence

This captures the transition from pure language statistics → to reasoning → to world understanding → to real-world action.

Along the way, RAG, memory, tool use, and multi-agent systems
serve as key bridges in this transformation.

✅ Summary Table

Dimension	Current Status	Early Signs?
Language Generation	Mature; probability optimized	✅
Context Understanding	Enhanced via CoT, RAG, long context	✅
Agentic Execution	Limited autonomy, prompt-driven	🚧
Multi-Agent Collaboration	Emerging ecosystems (CrewAI, MCP)	✅
General Intelligence (AGI)	Task-level generality only	🚧
Embodied Intelligence	Early-stage, mostly in simulation	🚧