Why RAG Isn't All You Need for AI Memory

The Essence of RAG

Conclusion: RAG is a retrieval method that sacrifices information precision for broader matching space. The sacrificed precision is the semantic richness (semantic loss[1]).

In principle, RAG works by converting text into vectors and finding "relevant" content through similarity calculations. During this process, rich semantic information is compressed into fixed-dimensional numbers, flattening subtle meaning differences.

All issues encountered in the industry stem from this semantic loss, such as:

Widening the Semantic Gap [2]
Low recall and hit rates, impacting practical applications [3]
Context loss: Document chunking leads to missing key contextual information [3]
Scalability challenges: More documents worsen vector retrieval accuracy [3]
Difficulty distinguishing similar documents: High-similarity docs are hard to differentiate [3]

For example, storing "I like eating strawberries" might retrieve "I like" first, then through multi-path recall find "I like eating grass," leading AI to conclude the user likes eating grass. This is a classic hallucination mechanism: fragmented retrieval plus erroneous splicing.

So, can this information loss truly be mitigated through technical improvements?

Why RAG's Problems Can't Be Solved by Improving RAG

Conclusion: Computation can never replicate semantic properties. This isn't an engineering issue but a philosophical divide.

Philosopher John Searle's "Chinese Room" thought experiment reveals this fundamental problem [4]:

Imagine a person who doesn't understand Chinese in a room with a detailed manual for processing Chinese symbols. When Chinese questions are slipped under the door, they follow the rules to find and output corresponding symbol combinations. To outsiders, it seems they "understand" Chinese, but they're just mechanically following symbol conversion rules, with no grasp of the meaning.

This exposes an ontological divide:

Computation/RAG World (Material)	Cognitive World (Ideal)
Symbols	Meanings
Vectors	Concepts
Algorithms	Understanding
Data	Experiences

Stevan Harnad's "Symbol Grounding Problem" [5] further illustrates: Imagine learning Chinese from a dictionary where all explanations are in Chinese. Looking up "horse" gives "a type of animal"; "animal" gives "a type of living being"... Symbols only point to other symbols, never reaching true meaning.

This is an ontological gap:

Computation is a physical process following causality
Meaning is a mental phenomenon in the realm of consciousness
The former is objective, measurable, deterministic
The latter is subjective, experiential, emergent

No matter how RAG is improved, it can't bridge this gap—like the most precise camera can't capture "beauty" itself, only the objects carrying beauty.

But does this mean RAG is useless?

What Is RAG Truly Suited For?

Conclusion: RAG is an excellent information retrieval tool, but not a cognitive system.

RAG is highly valuable for public information retrieval, much like search engines for humans—we don't directly apply search results but use our brains for analysis, reasoning, and learning. AI should do the same: Use RAG for external info retrieval, and a true memory system for cognitive processing.

Specifically, RAG suits:

Knowledge Q&A (fact-finding)
Document assistance (locating relevant passages in large docs)
As AI's "external reference book"

The key distinction: Retrieval finds information; cognition understands it. The former is RAG's domain, the latter needs a true cognitive system.

So, what does true AI memory require?

What Does AI Memory Need?

Conclusion: True AI memory requires preserving semantic integrity, understanding conceptual relationships, and supporting personalized organization.

Unlike RAG's fragmented storage, a true memory system needs:

Semantic Integrity: "I like eating strawberries" as a complete cognitive unit
Structured Relationships: Understanding "strawberries" as the object, avoiding erroneous splits
Personalized Organization: Each AI has its unique semantic network
Dynamic Evolution: Cognitive structures develop with accumulated experiences

This raises the core question: How to build a true AI individual cognitive system?

Monogent: AI Individual Cognitive System

Just as human children start from shared language and gradually form unique thinking patterns, AI needs such a development process. This isn't simple information storage but the construction and evolution of cognitive structures.

Monogent (AI Individual Cognitive System) is designed to solve this. What we need isn't better retrieval algorithms, but true understanding of:

What is memory
What is thinking
What is knowledge
What is experience
What is learning
What is innovation
What is cognition
What is individuality

In upcoming articles, we'll delve deeper into these questions.

About the Author

Deepractice - Making AI at Your Fingertips

This is the first in the Monogent theory series. Monogent is dedicated to building true AI individual cognitive systems, enabling each AI to have its unique cognitive world.

References

[1] Optimizing RAG with Hybrid Search & Reranking - Superlinked VectorHub

[2] The Rise and Evolution of RAG in 2024 - RAGFlow

[3] Overcoming Vector Search Limitations in RAG Workflows - Amity Solutions

[4] Searle, J. R. (1980). "Minds, brains, and programs". Behavioral and Brain Sciences. 3 (3): 417–424.

[5] Harnad, S. (1990). "The Symbol Grounding Problem". Physica D. 42: 335-346.