Let's start with a difficult title just to scare off newcomers:
“Contextualized Semantic Relevance with Dynamic Heuristic Weightings in Vector Preprocessing in Cognitive Environments with Limited Context Windows”.
Take a deep breath. Now forget that.
Let’s talk about something way more practical.
Imagine you're building a search system for an e-commerce platform with 150 million products. You type “smartphone” and, of course, you get phone cases, earbuds, screen protectors... everything but the actual smartphone.
If you've ever dealt with this, you already know: the issue isn't the lack of data. It's the overload of irrelevance.
Back in the day, when I worked with price comparison engines and search systems, the fix was simple and dirty: I built a scoring system where each word gained or lost points.
If the title had “case”, “protector”, “cover” → minus 100 points right away.
If it had “smartphone”, “iPhone”, “Galaxy” → plus 10.
And if those words were in the first three words of the title → score multiplier.
Finally, the product would get extra weight if it had a lot of clicks in the past few weeks.
The result?
Real smartphones came back to the top, and phone cases were buried in the irrelevant depths where they belong.
But what if we’re dealing with running text?
Now cut to 2024. You're playing with LLMs, using RAGs, vector embeddings, 128k token context windows, and you feel like a genius.
“Wait... the model still gives generic answers, forgets important context, and pulls in irrelevant documents even with FAISS or Qdrant.”
Yep. Because vectors don’t understand priority.
They can say “similar”, but they can’t say “important”.
Vectors are powerful. But dumb.
What’s missing is a layer of interpretation before the vector — something like my old search scoring system:
Extract the important terms from the sentence (“buy”, “iPhone 14”, “don’t want to spend much”)
Assign different weights based on intent, position, negation
Use that to filter the chunks that actually matter
Only then pass them to the model prompt
The fancy name for that would be:
“Symbolic-Semantic Hybrid Re-Ranking Before Contextual Vector Injection.”
But I just call it: common sense + good taste.
RAGs Are Dumb with Good Memory
RAG (Retrieval-Augmented Generation) is cool. It lets a model “remember” external things through vector search.
But the way most people use it, it’s basically:
input → embedding → top-k → prompt → answer“Give me the 5 most similar documents to what I just said, even if they’re totally useless.”
What we should be doing:
input → semantic analysis → heuristic re-ranking → embedding → promptIn other words:
Interpret what was said
Identify what actually matters
Prioritize and discard noise
Only then decide what goes into the model's context
Simple. But no one does it.
Why AGI Is Still a Meme
The AGI talk has become a tech-world mantra. Every new model triggers someone claiming: “GPT-5 will be AGI”, “DeepSeek already thinks like a human”, and so on. But the truth is: as advanced as these models may seem, they are nowhere close to general intelligence. And the reason is simple: their underlying architecture doesn’t support it.
Language models are great at predicting the next word. They access massive data, simulate complex conversations, answer questions, write code, poetry, even philosophize. But it all happens within a limited context window that, even when expanded (4k, 16k, 128k tokens), is still just a shallow memory simulation. Once the conversation ends, the model forgets. It doesn’t learn. It retains nothing. And even during the conversation, it only sees the immediate block of text you sent.
There’s no real memory. The model has no history, no sense of continuity. It doesn’t create new knowledge or update what it knows based on interaction. It simply receives a prompt and generates a response based on statistical patterns learned during training. That’s not intelligence — it’s extremely advanced autocomplete.
So we patch things: RAGs, vector databases like Qdrant, Pinecone, Faiss, agent architectures, tool-based flows. All to simulate memory and reasoning. But it’s still just stitching fragments together to make it look like the model “remembers” or “thinks”. The process still boils down to: generate the next most likely word.
The illusion that more context fixes everything also falls apart. You can have 128k tokens of window, but if the model can’t prioritize, can’t structure knowledge by relevance, intent, or purpose, it’s still just guessing. With more text. The size of the memory doesn't matter if it has no hierarchy, no focus, no goal.
Another bottleneck is hardware. Even with massive GPU clusters — 128GB VRAM each, hundreds running in parallel — models can’t function like a mind. They don’t simulate paths, test hypotheses internally, build layered interpretations, or evolve distributed knowledge. We have giant neural nets processing text efficiently, but structurally incapable of self-evolving.
As long as models are treated as static inference boxes, and everything revolves around embeddings + vectors + prompt engineering, we’ll keep going in circles. We’ll see bigger, faster, more expensive models — but not smarter ones. AGI won’t come from piling on tokens or stacking layers. It will come when we model systems that understand relevance, have goals, active memory, and evolve over time.
NLP and the Limits of Context
When we talk about advancing Natural Language Processing (NLP) toward something AGI-like, we always hit the same wall: limited context. Even 128k tokens are tiny compared to the amount of information a mind processes over days, weeks, or months. If the task is to handle billions of tokens of input, you can’t rely on the model’s memory.
That’s where vector memory sharding comes in — inspired by distributed databases. It assumes we can’t keep everything in RAM, let alone VRAM, but we can segment, index, and query relevant parts based on semantics and active context.
What we’re trying to build is a “fragmented mind” where each memory shard is split by domain, entity, time, intent — anything that allows that piece to be queried efficiently. In other words, a single flat vector store no longer cuts it. The real solution is smart sharding, where each memory segment knows who it should talk to.
But sharding alone isn’t enough. The bottleneck hits hard when you realize:
Most vector stores (Qdrant, Pinecone, Weaviate) are built for static semantic search.
In-memory FAISS helps, but blows up with 10 million vectors.
And the biggest gap: embeddings are blind to current conversation context.
You can have a perfect embedding, but if it was generated in a context where “Apple” was a fruit, and now you're talking about iPhones, the ranking may be useless. For it to work, the search needs to understand temporal and semantic weight of each term.
And beyond that: it’s not enough to return the 5 most similar vectors. The system must know why they’re relevant right now. That requires an intermediate layer that analyzes intent, time, entities, symbolic relevance, frequency of use. A kind of “memory cortex” that filters before it searches, scores before it ranks, and only then sends data to the model.
Embeddings Alone Aren’t Enough
Natural language is redundant, subjective, full of nuance. When you turn it into raw vectors, you lose the logical and symbolic structure that gives human reasoning its meaning. That’s why there’s increasing talk of hybrids between vector embeddings and symbolic memory — because vectors alone don’t capture hierarchy, causality, intent, or contradiction. They’re either similar. Or not.
To scale this kind of architecture, you start thinking like you’re building a cognitive database, where each memory chunk is an autonomous partition — semantically indexed, contextually updated, with dynamic access routes. And even then, the model needs a smart entry filter. Billions of vectors mean nothing if the model still relies on a flat prompt to make decisions.
The ideal setup would be a system where:
Natural language is transformed into a hybrid representation (tokens, vectors, symbols)
Each memory shard handles a specific knowledge domain or context
Queries go to shards not just by similarity, but by inference need
And the LLM acts as an orchestrator — not the brain, but the central processor in a network of intelligent memories
As long as language models are treated as closed boxes with linear context, true cognition or general reasoning will remain out of reach. The solution isn’t “more tokens.” It’s better semantic, contextual, and functional memory organization. Something that thinks like a distributed mind — not carrying everything all the time, but knowing exactly where to look, when to look, and what to prioritize.
“But quantum computing will solve all this, right?”
It won’t.
Quantum processors, in theory, massively boost parallel computation, which can help in some tasks — like molecular simulations, combinatorial optimizations, or eventually speeding up neural network training. But they don’t solve the core AGI problem: memory architecture and component communication in a functional artificial mind.
The bottleneck isn’t in pure calculation. It’s in how data is stored, accessed, updated, and reused across reasoning chains. And qubits don’t help with that. Even if you can compute a thousand paths at once, if you can’t structure and recall them properly, it’s useless.
It’s like having a super brain that forgets everything it thought in the last sentence.
The real hardware revolution — if we want a viable AGI path — needs to go in a different direction. We need RAM that stores trillions of vectors, with real-time access and latency as low as current VRAM. As long as CPU/GPU is separated from memory, with bus bottlenecks slowing data transfer, you’ll never have a functional mind running billions of interactions in real time.
The architecture of the future isn’t a magical quantum chip.
It’s a system where memory and processing coexist with near-zero latency, and where storage scales with reasoning.
Until then, what we have are simulations. Brilliant models, impressive benchmarks — but limited by design.
It’s not a lack of intelligence.
It’s a lack of structure.
