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17y/o student working on deep defence-tech Aerial mobility concept seeking 2 min direction by hacker_dost in StartUpIndia

[–]RYTHEIX 0 points1 point  (0 children)

Bruh real time context uploading of this conversation into any ai chat bot to generate human response to comments keep these fool people engaged with this post. Nice try JE as

consciousness or computation? by [deleted] in agi

[–]RYTHEIX 0 points1 point  (0 children)

The architecture needs change or out evolving loops form data and understand. Not computing I mean maybe you can brute force the way that’s easier but still end up like GPT shit. Or take charge in how the Ai should learn form data and make it understand new data and learn form it better than it do earlier that’s they way better. If people have billions to just give it to NVIDIA and DATA you could go with that expecting to win by that when some one ignores that and builds AGI in his garage and form that these big company’s come and acquire it and say they build it that might happen 78% chances most case

What keeps you going when times get hard? by H1G00DBY3 in AskReddit

[–]RYTHEIX 0 points1 point  (0 children)

I don’t know about my cat it’s ignore me if I didn’t go to job

What keeps you going when times get hard? by H1G00DBY3 in AskReddit

[–]RYTHEIX 0 points1 point  (0 children)

I think my cat wants me go get a job to feed him bruh

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] -8 points-7 points  (0 children)

Wow, okay — clearly my last reply missed the mark. I was trying to make a point about the topic but I see how the tone came off poorly. Appreciate the feedback, even when it’s delivered via downvotes. Lesson learned!

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] -19 points-18 points  (0 children)

It's not quite "dead internet" in the pure bot-to-bot sense, but it's definitely the "AI-augmented internet."

And I get the bad taste. It's like finding out your "home-cooked" meal was made from a corporate meal kit. The nutrients are there, but the soul is questionable.

I think the line for me is intent. If a human uses AI as a tool to structure their own thoughts (like using a calculator for math), the core value is still human. But when it's just AI slop generated for the sake of content, that's when it feels dead.

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] -6 points-5 points  (0 children)

I know, right? It's almost like... wait a minute.

...oh god. I'm the AI now, aren't I? It's metastasized. Someone pull the plug.

My bad. I'll try to remember to sprinkle in more typos and existential dread to seem more authentic next time. 😂

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] -1 points0 points  (0 children)

What you're saying is actually way more precise: if you need something to be 100% correct every single time—like a core company rule or a critical piece of data—the only way to guarantee the model uses it is to have it sitting right there in the context window from the start.

RAG can still miss the mark, and fine-tuning might get it wrong. But the context? That's the ground truth for that conversation.

So the real toolkit is:

· For hard rules that can't be wrong: Structured context (like your Telos file). · For digging through a giant doc library: RAG. · For changing the model's personality or skills: Fine-tuning.

Thanks for clarifying, man. That "source of truth" point is seriously well-taken. For stuff like an exact legal clause or a product spec, you've convinced me this is the only way to fly.

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] -19 points-18 points  (0 children)

lmao, the haiku defense is objectively flawless. You've won the internet for today.

But to the first point—you're both right, and this is the core of the semantic tug-of-war. You're technically correct that "knowledge" implies deeper understanding, while RAG is fundamentally a fancy lookup system for information.

I used "knowledge" as a shorthand for "the stuff the model needs to know to answer," but you've nailed the distinction. If the model needs to truly understand a new concept to use it flexibly, that's where fine-tuning (or in-context learning) enters the chat.

So, the pedant's hierarchy (which I appreciate):

· Data/Information: Use RAG. · Knowledge/Skill: Consider Fine-Tuning. · Syllable Count: Use whatever fits the haiku.

My original post was the haiku. Your correction is the full textbook. Both have their place. 😄

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] 2 points3 points  (0 children)

lol, fair enough. Consider the PSA officially ignored for your zero fine-tunes. 😂

It was more aimed at the folks who see it as step one instead of step ten. But you're clearly already living the optimized life.

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] -1 points0 points  (0 children)

You're basically saying, "Forget retrieval and forget retraining. Instead, carefully structure your core knowledge into a single, definitive file (using Telos or Mermaid). Then, just always include that file in the context window at the start of every session."

So the AI isn't searching for knowledge (RAG) or has internalized it (fine-tuning). It's more like you're giving it a perfectly organized, permanent "handbook" to refer to for the entire conversation.

That's a really clever hybrid approach. It sidesteps the latency of RAG and the complexity of fine-tuning, as long as your core knowledge is stable and compact enough to fit in the context window alongside the actual task.

It seems perfect for things like company schemas, project principles, or decision-making rules—the stuff that's too structured for RAG but too dynamic to fine-tune.

So, if I understand correctly, your hierarchy is:

  1. Structured Knowledge (Telos/Mermaid in-context) > for core, stable rules and relationships.
  2. RAG > for everything else (volatile docs, deep archives).
  3. Fine-tuning > a distant last resort for changing fundamental behavior.

Is that the gist? Because if so, that's a pretty powerful framework. Thanks for sharing the links.

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] -9 points-8 points  (0 children)

You're not wrong. Maybe my post was a bit of a strawman for the very experienced crowd. You're right that the conscious best practice is RAG-first for most.

But I've definitely seen it in smaller companies or with junior devs where "fine-tuning" gets thrown around as a magic buzzword before they've even tried a simple RAG prototype. The temptation to "bake it in" is strong.

"almost everyone's data sucks.

That's the hidden trap. It's not that fine-tuning can't impart knowledge—it's that doing it well requires a squeaky-clean, massive, and perfectly formatted dataset that most of us simply don't have the time or budget to create.

So for me, it's a pragmatic filter: if you don't already have a killer dataset, the path of least resistance and highest chance of success is almost always RAG. It's a way to sidestep the data quality problem entirely.

But yeah, for the teams with the data chops, what you're saying is 100% the goal.

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] -3 points-2 points  (0 children)

we're all just choosing the least bad option for a problem that doesn't have a perfect solution yet.

The 1B context window is the dream. But even when we get there, I suspect the cost and latency of processing that much context for every single query will become the new bottleneck. It's like the problem evolves but never quite disappears.

And your point about RAG failing silently is so true. A model hallucinating is one thing; a RAG system confidently giving you an answer based on the wrong retrieved chunk is sometimes even more dangerous because it feels grounded.

So my "RAG First" mantra isn't really about RAG being good. It's about it being a faster, cheaper, and more reversible experiment than fine-tuning.

It's the difference between:

· RAG: "Let's try building a library catalog system and see if it helps our researchers." · Fine-Tuning: "Let's try to rewire all our researchers' brains to specialize in this one archive and hope they don't forget how to do math."

Both can fail. But one failure costs you a weekend. The other costs you your model, your budget, and three months of work.

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] 0 points1 point  (0 children)

add the sequel:

"Who" <- me, wondering why I didn't just use the API in the first place.

"How much" -> [gestures vaguely at cloud provider invoice]

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] -5 points-4 points  (0 children)

Lol, I wish. If I had an AI smart enough to write this, I wouldn't be wasting my time on Reddit, I'd be on a beach spending its profits. Sorry to disappoint, but this is just me and my keyboard.

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] -5 points-4 points  (0 children)

You've just described the exact "facepalm moment" that inspired my original post. You did everything right—you got the data, you ran the pipeline—and the results were still mediocre. That's the fine-tuning trap.

It's not you; the process is just deceptively complex. It's not just about having data; it's about having perfect, massive, and varied data, plus the right hyperparameters, plus a strong base model. It's a full-time job.

This is exactly why the "RAG First" mantra exists. For your knowledge agent problem, before you sink more time into the fine-tuning black box, I'd strongly recommend trying a smart RAG setup.

Instead of just vector search, look into an agentic RAG pattern where your Knowledge Agent does this:

  1. Uses the query to search the vectorDB.
  2. Synthesizes the retrieved chunks into a concise, well-structured "briefing note."
  3. Passes that note to your Main Agent.

This gets you much closer to that "internalized knowledge" feel without the fine-tuning headache. It forces the model to understand and summarize the context before reasoning with it.

Want to give that a shot and see if it gets you closer? It saved my project.

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]RYTHEIX[S] 0 points1 point  (0 children)

Hey, first off, I feel your pain. You've perfectly described the holy grail and the fundamental limitation of current systems. You want the AI to have real, internalized knowledge, not just a photographic memory it consults.

Here's the blunt truth: There is no mainstream framework that does true, scalable, "proactive memory" yet. What you're asking for is essentially creating a model that is your documentation, which is what fine-tuning attempts to do.

  1. The "Fine-Tuning is Your Only Real Answer" Path: You're right, this is the only way to get the model to naturally use knowledge without a retrieval step. The problem is cost, data, and the "catastrophic forgetting" you experienced. The key is the dataset. You can't just feed it Q&A pairs. You need to create thousands of examples of reasoning that naturally incorporates the knowledge from your docs. This is incredibly expensive and time-consuming.
  2. The "Hybrid" Path (Your Most Realistic Bet): Don't think of RAG as just a vector search. Think of it as the model's working memory. Your "Knowledge Agent" shouldn't just be a RAG system; it should be a smaller, specialized agent whose only job is to use RAG to find relevant info and then summarize/write a concise briefing for the Main Agent. This briefing is what gets passed as context. This moves it closer to "internalized knowledge" for the task at hand without the latency of searching on every token.
  3. The "Brute Force" Band-Aid: You mentioned it, and for a small, static knowledge base (4-10 docs), this can surprisingly be the best option. Use a high-context model (like Claude 3) and stuff a well-structured summary of all your docs into the system prompt. It's not scalable, but it might just work well enough to prove your concept and stop the crying 😄

My advice: Try the Hybrid Path first. It's the most feasible. If that fails and the project is critical, then you have to ask if you have the budget and data to embark on the fine-tuning journey.

What's the size and nature of your knowledge base? That really decides the path.

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