Is AGI even possible without moving beyond vector similarity?

Is Achieving True Artificial General Intelligence Possible Without Advanced Vector Similarity Techniques?

In our ongoing pursuit of more sophisticated AI, we’ve made remarkable progress—particularly in utilizing large language models (LLMs) that interpret embeddings and generate coherent text-based responses. However, this advancement often comes with significant challenges, especially related to token limits and the limited context window within LLMs, which hampers tasks such as retrieval-augmented generation (RAG) and complex data searches.

Despite these developments, there remains a fundamental issue: the effectiveness of similarity search, especially vector-based methods. Modern LLMs have transformed traditional machine learning techniques—those fundamental algorithms once used for normalization, one-hot encoding, and rigorous data analysis—into powerful but sometimes opaque systems. Today’s developers often favor integrating LLMs and generative AI directly into the data pipeline rather than meticulously preprocessing data, sparking debate about accuracy and reliability.

Consider a typical use case: querying a dataset containing thousands of customer records or product descriptions. The conventional approach might involve generating an SQL query via an LLM to retrieve relevant data. But what happens when the query relates to product attributes rather than specific identifiers? Static embedding models—while useful—can struggle to accurately match semantically similar content, especially when queries are phrased differently or are less structured.

This leads us to question the very foundation of our retrieval mechanisms. Before we even approach true Artificial General Intelligence (AGI), we must address whether current similarity search methods are sufficient. Are they genuinely capturing the nuanced meanings within data, or are they just measuring geometric closeness in high-dimensional space? The latter has notable limitations:

• Irrelevant or superficial matches when queries are ambiguous or poorly phrased.
• Fragility to rephrasing, where slight changes in wording can lead to different retrieval results.
• An inability to grasp deeper semantic relationships, especially in sparse or highly structured data contexts.

Key Takeaway:
While large language models may seem remarkably intelligent, the core component of RAG—the vector retrieval layer—often remains simplistic, relying on geometric proximity rather than semantic understanding. This approach works well for dense lexical overlaps but falls short when interpreting intent across varied or sparse data domains.

Final Thoughts:
Advancing towards AGI may not only require more powerful models but also a fundamental improvement in how we perform similarity searches. Developing robust, semantically aware retrieval methods could be the key to bridging the gap, enabling AI systems to understand context more deeply