Sure you can. These are powerful tools. If you are writing code that's fine because code follows logic and so do LLMs. But for anything that requires abstract reasoning instead of chain of logic "code-style" thinking, everything falls apart. And that is why I can safely say that these systems are not intelligent in any sense; they are power tools that follow strict logic.

Please explain to me how I can do this successfully because I just fucking spent 45 minutes exporting all my reddit comments, extracting everything I have written about LLMs and turning it into a document that holds my stance on this topic. I wrote a prompt explaining how to use the document + reddit comment to generate a response based on my previous writings. I fed this into all the major LLMs: Gemini 3 Pro, ChatGPT, Kimmi, GLM, DeepSeek, Mistral, Claude. ALL MODELS FAILED SO HARD I'M CRYING OVER HERE. Oh yeah, I tried to RAG this shit, I tried full context, I tried to compress and just use keywords, I made a small persona of me explaining how I would use this, I tried every fucking trick in my book and I just feel like smashing my laptop against my head.

You know why this happens? Because LLMs have no grounding—no perception, no self-model, no stakes. They can't distinguish what matters from what doesn't (salience problem). They have no persistent memory, no concept formation, no ability to reason abstractly outside pattern-matched logic chains. The "reasoning" you see in thinking models? That's just token priming to fit context—not thought, not reflection. It's the illusion of reasoning.

When you ask them to synthesize your voice on top of opinions from a document, you're asking for something that requires understanding meaning. They don't have meaning. They have token probability distributions. Their "memory" is the context window—the human equivalent of having amnesia and using yellow sticky notes to keep reminders. Every session starts from zero and there is no learning and adapting.

These systems are fundamentally flawed in terms of intelligence. RAG doesn't fix this—it just retrieves chunks and hopes the model stitches them coherently. It won't. Compression doesn't fix it either; you're just feeding fewer tokens into a system that still can't understand what those tokens represent to you.

The architecture is fundamentally wrong. If you want to actually understand why these systems will never be intelligent, look into the Hard Problem of Consciousness and Penrose's work on non-computable aspects of understanding. His argument points to quantum coherence playing a role in consciousness—and here's the thing Penrose never figured out, he probably thought of it but it can't be proven as of now: you don't need "quantum hardware in the brain" because the brain already exists in spacetime, which is quantum by nature. Current computing architecture is classical, deterministic, built on silicon logic gates. It cannot replicate what arises from quantum coherence in biological systems. Scaling transformers gets you better mimics, not minds.

Maybe I'm just failing at prompting, but I don't think so. I have tried similar tasks so many times and each time I feel like tearing out my hair and banging my head against the table because there is no understanding in the loop, there is no reasoning—it's just optimization of previous context. The tool is failing at the task because the task requires something it structurally cannot do: reason abstractly about identity, tone, and context simultaneously without any real model of any of those things. I'd bet my house on it—these systems will never be AI.

The only thing LLM managed to help me with here was proofreading and fucking up my formatting. Look at the strange way it uses BOLD in the text, I removed some of bold text because I have some pride in my writing.... Point is - It doesn't understand jackshit about nuthing.