LLMs Are Not All You Need

Large Language Models (LLMs) exhibit remarkable intelligence, but they lack crucial subsystems necessary for higher-order reasoning, particularly when applied to external datasets of any meaningful scale.

When fed external data, an LLM will process it and generate a response. However, there is no concept of a memory aside from a temporal cache for optimization—therefore, any form of long-term memory must be managed externally. In conventional chat systems, this memory is what we see when viewing the conversation history.

But conversation history is limited and there are several more advanced solutions: Retrieval-Augmented Generation (RAG), expanded context windows, and external AI-based memory systems. Engramic operates at the frontier of this space, with the goal of innovating on the current methodologies.

The Limits of Existing Approaches

RAG systems, while not new, struggle with scalability when dealing with large datasets. The primary obstacle, we believe, is in how data chunks are structured, stored, and embedded. Simply put, retrieval quality and inference accuracy depend not just on the content of these chunks but on the context surrounding them, which most systems neglect. Engramic, named after the concept of an engram or memory trace (both from neuroscience and cognitive psychology), enhances the context of data chunks, vastly improving retrieval precision and inference accuracy.

Expanding the context window alone does not resolve the problem—it merely increases the volume of information, not the model’s ability to meaningfully retain and prioritize details. As context windows expand and as they are filled, attention dilutes, due in part to the computational limits of transformer models. Solving this issue will require a fundamental innovation, beyond sparce attention, in how LLMs process information. Context windows, like other aspects of nature, isn’t just about the size.

Similarly, external AI memory systems introduce their own imperfection. LLMs already exhibit lossy recall, and when paired with an external long-term memory system who also exhibits lossy recall, errors compound. The result is a memory that—like human memory—is constructive rather than precise. Shouldn’t we aim to surpass human memory, which sacrifices accuracy for speed, in terms of accuracy?

The Engramic Approach

Engramic is a RAG system, but it builds upon traditional implementations in several ways:

  1. Long-Term Memory – Engramic stores representations of responses, they are validated, relevant, provided a valuable reframing of fact-matter.

  2. Enhanced Context Integration – Context is not an afterthought but an integral part of the data ingestion and consolidation process. The system ensures that context enriches each data chunk, improving both retrieval and inference accuracy.

  3. Conceptual Combination – Stored engrams become part of an evolving dataset, enabling the system to recall, combine, and synthesize ideas. This approach fosters insight-driven responses—akin to human aha moments—and facilitates higher-order cognitive functions like analysis and evaluation. It is particularly valuable when cross-pollinated with relevant external data or experience.

  4. Step-Function Improvements – No single enhancement solves the problem outright. Engramic benefits from a series of refinements across the RAG pipeline, incrementally reducing errors and improving response precision. Achieving near-perfect recall—pushing toward 99.999% accuracy—in a inherently probabilistic system, requires careful orchestration.

Engramic is not merely an incremental improvement—it represents a necessary evolution in how AI systems process, store, and retrieve knowledge. While LLMs provide the foundation, they are not all you need.