Welcome back to My Weird Prompts, the podcast where producer Daniel Rosehill sends us fascinating questions and we dive deep into them with absolutely no preparation whatsoever. I'm Corn, and I'm joined as always by Herman Poppleberry. Today we're tackling something that's been on Daniel's mind lately - the role of system prompting in AI, how it compares to fine-tuning, and whether all this infrastructure we're building around fine-tuning is actually solving real problems or just creating solutions looking for problems.
Yeah, and what I find particularly interesting about this prompt is that it gets at something fundamental about how we interact with AI systems that I think a lot of people overlook. Everyone's focused on agents and MCP and autonomous systems, but the unglamorous work of actually steering model behavior? That's often happening in a system prompt, and it's kind of invisible.
Right, exactly. Like, Daniel mentioned that he got really enraptured by the idea that you could use natural language instead of code to create powerful systems. And I think a lot of people don't realize that's... kind of wild when you think about it. You're literally just writing instructions in English and the model goes, "Okay, I'll do that."
Well, hold on. I want to be careful about how we frame this, because saying "you're just writing instructions" kind of undersells what's actually happening. It's not magic - there are real mechanisms at play. The system prompt is being processed by the model in a specific way, and it does genuinely constrain behavior, but it's not like you're rewriting the weights or fundamentally changing the model's architecture.
Okay, but like... it kind of does work though. Daniel mentioned he tried system prompts that said things like "respond with the minimum possible" or "only answer yes or no," and the model actually adheres to that. That's not nothing.
It's not nothing, but I'd push back on the framing that it's simple. What's happening is that you're providing context that the model uses to make predictions about what tokens should come next. The model has been trained on examples of concise responses, verbose responses, yes-or-no answers, all of it. So when you add a system prompt that says "be concise," you're essentially biasing the probability distribution of what comes next toward shorter, more direct tokens. It works because the capability is already there in the model.
Okay, so it's like... the model already knows how to be concise, and the system prompt is just saying, "Hey, use that capability now."
Exactly. And that's actually why system prompting is so powerful but also why it has limits. You can't use a system prompt to make a model do something it was never trained to do. If the base model doesn't have the capability, no amount of clever prompting will create it from nothing.
Right, which makes sense. So if a model has never learned to write in Klingon or calculate in base-7 or whatever, you can't just prompt it into doing that. But here's what I'm curious about - if that's true, then why is fine-tuning even necessary? Like, couldn't you just... train the model better in the first place?
Well, that's a different question. Fine-tuning is about specialization. You take a general-purpose model and you adapt it to a specific domain or task. The infrastructure around fine-tuning has exploded in 2024 - there are all these platforms now that make it accessible. But here's the thing that I think Daniel's getting at in this prompt: we're not entirely sure who's actually benefiting from it in practice.
Yeah, that was interesting. Daniel said he sees questions on Reddit and YouTube like, "Has anyone actually found a good reason to fine-tune?" And that's... a pretty damning question for all this infrastructure that's been built, right?
It is, but I think the answer is more nuanced than it might seem. Fine-tuning is genuinely useful in certain domains. Medical specialties, for instance - there's research showing that fine-tuning LLMs for specific medical subspecialties improves accuracy significantly. Sentiment analysis, named entity recognition, specialized summarization tasks - these all benefit from fine-tuning. But you're right that there's this weird gap between the hype around fine-tuning and the actual adoption.
So what's causing that gap? Is it that the use cases are niche?
Partially. But I think it's also a matter of cost-benefit analysis. Fine-tuning requires you to have good training data, which is hard to come by. It requires infrastructure and expertise. It's more complex than prompt engineering. And here's the thing - in a lot of cases, a really well-crafted system prompt gets you 80% of the way there for a fraction of the effort.
But isn't that exactly what Daniel's asking though? Like, how do we know when we've hit the ceiling of what system prompting can do? How do you know when it's time to actually fine-tune?
That's the million-dollar question, honestly. And I don't think there's a clean answer. But we can talk about the trade-offs. System prompting is fast, cheap, and reversible. You can iterate rapidly. Fine-tuning is slower, more expensive, and requires commitment. But fine-tuning can achieve higher accuracy on specialized tasks because you're actually modifying the model's learned behaviors through additional training on domain-specific data.
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...Right. Thanks, Larry. Anyway, where were we? Oh yes, the actual mechanics of this.
So let me try to ground this in something concrete. Daniel mentioned he had his first experiences with fine-tuning in the ASR domain - that's automatic speech recognition, right? What was he doing there?
ASR is actually a pretty good example because it's a domain where fine-tuning has proven its worth. If you want a speech recognition model to understand a specific accent, industry jargon, or acoustic environment, fine-tuning on domain-specific audio data genuinely improves performance. You can't just prompt your way to understanding medical terminology in a thick Scottish accent - the model needs to have learned that pattern.
Okay, so that makes sense. But then why does that not translate to language models? Like, why can't we just fine-tune every LLM for every specialized domain?
We could, but the economics don't work. Speech recognition has a clear value proposition - if your transcription accuracy goes from 85% to 95%, that's a huge win. Language models are more... fuzzy. It's harder to measure whether you've actually improved performance, and the cost of fine-tuning has to be justified against the benefit. For a lot of use cases, prompt engineering and system prompts get you good enough results.
But "good enough" is different from "optimal," right? I mean, if you're a financial services company and you need consistent, specialized behavior from an AI system, wouldn't you want to fine-tune?
You might, and some do. But here's where I think the infrastructure question comes in. There's been this massive investment in fine-tuning platforms and services. OpenAI has fine-tuning capabilities, Anthropic has them, there are startups building entire businesses around it. But the demand hasn't quite materialized the way the supply suggests it should. That's what Daniel's picking up on.
So is it a chicken-and-egg thing? Like, did we build the infrastructure before we understood the actual use cases?
I think there's some of that, yeah. But I also think there's something else happening. The industry recognized that fine-tuning would be important, so they built tools for it. But they didn't necessarily anticipate how good system prompting would get, or how much you could accomplish with clever prompt engineering and the Model Context Protocol.
Oh, that's a good point. Daniel mentioned MCP in the prompt - the Model Context Protocol. Anthropic released that in November 2024, right?
Right, and this is actually crucial context for this conversation. MCP is changing how agents interact with tools and context. It streamlines agent interactions and reduces context usage. And here's the thing - MCP actually might be a partial answer to some of the problems that people thought fine-tuning would solve. If you can give an agent access to the right tools and context through MCP, you might not need to fine-tune the base model at all.
So you're saying MCP could be a substitute for fine-tuning in some cases?
In some cases, potentially. Not all. But the point is that the landscape is shifting faster than people anticipated. We have better ways to constrain and direct model behavior now - through system prompting, through tools and context, through MCP. That changes the calculus around fine-tuning.
But doesn't that mean we should be having this conversation about what fine-tuning is actually for? Like, let's define the actual use cases where it makes sense.
Absolutely. And I think there are real ones. Medical specialization is one. Financial modeling and analysis - there are companies that have built proprietary datasets and fine-tuned models for specific financial tasks. Legal document analysis in specialized areas of law. These are domains where you have:
One, high-value tasks where accuracy really matters.
Two, domain-specific data that you own or can access.
Three, a clear performance metric you're trying to optimize.
Okay, so those are the criteria. But most companies probably don't have all three of those things, right?
No, most don't. Most companies have maybe one or two. And for those cases, system prompting plus good tool integration through something like MCP is probably sufficient.
So circling back to Daniel's original question - why is system prompting so effective even though it seems so simple? You're not changing the weights, you're not retraining anything. You're just writing instructions.
Right, and I think the answer is that the model has been trained on vast amounts of text that includes examples of following instructions. The system prompt is just one more piece of context that the model processes. It's not magic, but it's also not trivial. The model has learned to be sensitive to instructions, to adapt its behavior based on framing and context.
But here's what I don't quite get - and I think this is where Daniel's skepticism is coming from - how does that work mechanistically? Like, how does writing "respond concisely" in a system prompt actually change the probability distribution of the tokens that come out?
That's a great question, and honestly, we don't have perfect visibility into this. The models are trained end-to-end, and the system prompt is just part of the input. During training, the model learned patterns about what kinds of responses follow what kinds of prompts. When you add a system prompt at inference time, you're essentially activating those learned patterns. It's like... imagine an actor who has learned to play both comedic and dramatic roles. If you tell them, "This is a comedic scene," they'll automatically adjust their performance because they've learned the patterns of comedy. The system prompt is that direction.
Okay, I like that analogy. But here's where I push back a little - Daniel mentioned he's tried to get the model to respond with just "yes" or "no," and it actually works. But that seems like a pretty strong constraint. How does the model learn to be that constrained?
Because it has seen examples of yes-or-no responses in its training data. It understands that constraint. And here's the thing - that's actually a really good test case for the limits of system prompting. If you ask a model to respond in a way it has genuinely never seen in training data, the system prompt won't work. But yes-or-no responses? That's a pattern the model has seen millions of times.
So system prompting works because it's activating existing capabilities, but fine-tuning is about creating new capabilities?
I'd say it's more about strengthening and specializing existing capabilities. Fine-tuning isn't creating something from nothing either. You're taking a pretrained model and showing it more examples of the specific kind of behavior you want. You're essentially saying, "Here are a thousand examples of how to analyze medical documents. Learn from these." The model updates its weights slightly to be better at that specific task.
And that updates the weights in a way that a system prompt can't?
Exactly. A system prompt operates at inference time. Fine-tuning modifies the model itself. So if you fine-tune a model on medical documents, it becomes fundamentally better at analyzing medical documents, even without a system prompt telling it to do so. That's a permanent change to the model.
But that also means you have to deploy a different model, right? You can't just swap it out in your system prompt like you can with instructions.
Right, and that's part of why system prompting is so appealing. It's reversible, it's fast, it doesn't require redeployment. But that also means it's limited to activating capabilities that are already there.
Okay, so here's a practical question. If I'm building an AI system right now, in 2024, and I want specialized behavior - let's say I want an AI assistant for a specific industry - when do I start with system prompting and when do I go straight to fine-tuning?
I'd always start with system prompting. It's faster, cheaper, and you learn a lot about what you actually need. If you can get 90% of the way there with a really well-crafted system prompt, why spend weeks and money on fine-tuning? But if you hit a wall - if you're consistently getting results that aren't good enough - then you evaluate fine-tuning.
But how do you know you've hit the wall? How do you know you're not just not being creative enough with your system prompt?
That's fair. You could be. But there are some signals. If you're trying to get the model to understand very specialized terminology or concepts that don't appear much in the training data, system prompting probably won't cut it. If you're trying to get consistent, reproducible behavior on a narrow task where accuracy matters a lot, fine-tuning might be worth it. If you're trying to change the fundamental style or approach of the model - like, make it always reason step-by-step in a specific way - fine-tuning could help. But honestly, a lot of that can be accomplished with really good system prompting too.
So you're saying the line between them is blurry?
Very blurry. And I think that's actually the honest answer to Daniel's question about why there's all this infrastructure for fine-tuning but people aren't sure if they need it. The infrastructure got built because the industry thought fine-tuning would be a big deal. But in practice, system prompting has turned out to be more powerful and more flexible than people anticipated.
But that doesn't mean fine-tuning is useless, right?
No, not at all. It's useful. It's just more niche than the hype suggested. And here's what's interesting - and I think this is something Daniel's picking up on - we might be entering a phase where the real innovation is in how you combine system prompting, tool integration through MCP, and selective fine-tuning. Not one or the other, but all of them together.
So like, you have a base model, you give it the right system prompt, you integrate it with tools through MCP, and then maybe you fine-tune on some specific domain data?
Potentially, yeah. Though I'd say most people would get great results with the first two. The fine-tuning is the cherry on top for high-value, specialized tasks.
Alright, we've got a caller on the line. Go ahead, you're on the air.
Jim: Yeah, this is Jim from Ohio. I've been listening to you two dance around this for the last fifteen minutes and I gotta tell you, you're overcomplicating it. And you know, my neighbor Randy does the same thing - he spent three hours trying to figure out which air filter to buy when he could've just grabbed any of them. Anyway, here's my point. System prompts work because they work. Fine-tuning is just for people who like spending money on complicated solutions. You don't need either one if you just ask the question right the first time.
Well, I appreciate the feedback, Jim, but I think there's actually more going on under the hood than just "asking the question right."
Jim: Yeah, but see, that's exactly the problem. You're making it complicated. In my day, you just told the computer what to do and it did it. Now you've got all these fancy terms and frameworks and... I don't know, it just feels like people are making problems so they can sell solutions. Also, my blood pressure has been acting up lately, so I'm already irritable. But seriously, why can't people just keep things simple?
I understand the skepticism, Jim, but the thing is, these models are fundamentally different from the computers you're used to. They're probabilistic. They don't just follow instructions - they generate text based on learned patterns. System prompting works, but understanding why it works and when it doesn't is actually important if you want to use these systems effectively.
Jim: Ehh, you're still overcomplicating it. My cat Whiskers understands instructions just fine without all this theoretical nonsense. She knows to get off the kitchen counter when I tell her to. Why can't your AI models be that straightforward?
Well, because cats have learned behavior through experience and punishment, and LLMs have learned through statistical patterns in text. It's actually a pretty different mechanism.
Jim: Look, all I'm saying is that you two sound like you're trying to justify why fine-tuning exists when maybe it just doesn't need to. Keep it simple. Thanks for having me on, but I'm not convinced you've made your case. I'm going back to my coffee.
Thanks for calling in, Jim. We appreciate the perspective.
Yeah, and look - Jim's not entirely wrong. There is a tendency in the industry to overcomplicate things. But I'd argue that understanding these mechanisms is what prevents overcomplification in practice.
That's a fair point. So let's talk about what someone should actually do if they're building a system right now. What's the practical takeaway?
Okay, so here's my recommendation. Start with system prompting. Invest time in writing good prompts. Test them. Iterate. See how far you can get. If you're building something for a specialized domain, layer in tool integration - MCP is good for this. Give your system access to the right context and capabilities. Then, and only then, if you're still not getting the results you need, consider fine-tuning. But be honest about whether you're hitting a real limitation or just need to be more creative with your system prompt.
And what's the timeline for that? Like, how long should you spend on system prompting before you give up and fine-tune?
Depends on the task, but I'd say weeks, not months. If you're spending more than a couple of weeks iterating on system prompts without progress, then maybe fine-tuning is worth exploring. But most people don't spend nearly enough time on the prompt engineering phase before they jump to fine-tuning.
And what about cost? That's a real factor for a lot of companies.
System prompting is basically free - it's just your time. Fine-tuning has real costs. You need training data, you need compute, you need infrastructure. It's not huge, but it's not nothing. So yeah, cost is definitely a reason to exhaust the system prompting avenue first.
What about the Model Context Protocol? Should people be thinking about that now?
Absolutely. MCP was released in November 2024, and it's becoming the standard way for agents to interact with tools and context. If you're building agentic systems, you should be thinking about MCP. It actually changes what's possible with system prompting and tool integration, because it makes it easier and more efficient to give your agent access to the right context and capabilities.
So MCP is kind of the glue that ties system prompting and tool integration together?
Exactly. It's a protocol that standardizes how agents interact with their environment. Code execution with MCP is more efficient, uses fewer tokens, reduces context. That's huge for building effective systems.
Alright, so the takeaway is: system prompting is more powerful than people realize, fine-tuning is more specialized than the hype suggests, and MCP is changing the game for how agents interact with their environment.
I'd add one more thing. This stuff is evolving really fast. Six months from now, the landscape might look different. We might have better tools, better models, better ways to constrain and direct behavior. So the advice I'm giving today should be taken with that in mind.
That's a good caveat. And I think that's actually part of what Daniel was getting at in this prompt - there's a lot of infrastructure and hype around fine-tuning, but the fundamentals of what works and what doesn't are still being figured out.
Right. And I think as the industry matures, we'll get clearer answers. We'll see which fine-tuning use cases actually pan out, which ones were hype, and what new approaches emerge. But right now, we're in a phase where system prompting is doing a lot of heavy lifting.
So if someone's listening to this and they're curious about experimenting with system prompting, what should they do?
Pick a task you care about. Write a detailed system prompt that explains exactly how you want the model to behave. Test it. Try variations. See what works. Don't assume you need fine-tuning until you've really explored what's possible with system prompting. And if you do find yourself wanting to fine-tune, be honest about why. Is it because you've genuinely hit a limitation, or is it because you heard it's the cool thing to do?
Yeah, and I think that honesty is important because there's definitely some cargo cult stuff happening in AI right now. Like, people see that fine-tuning exists and assume they need it, without really thinking about whether it solves their problem.
Exactly. And that's why understanding the difference between system prompting and fine-tuning, and when each is appropriate, is actually really valuable. It prevents you from wasting time and money on solutions you don't need.
Alright, so as we wrap up, I think the key insight here is that system prompting is underrated and fine-tuning is maybe overhyped. But both have their place, and the smart approach is to start simple and add complexity only when you need it.
I'd agree with that. And I'd add that the whole landscape is changing with things like MCP. The tools we have now for controlling and directing AI behavior are getting better and more sophisticated. So even if you think you need fine-tuning today, you might not need it six months from now because there's a better way to do what you're trying to do.
That's a really good point. Thanks to Daniel Rosehill for sending in this prompt. It's exactly the kind of question that gets at the real substance of how these systems work, beyond the hype and the marketing.
Yeah, and I think it's a question more people should be asking. Not just "Can I fine-tune?" but "Do I actually need to?" There's a lot of wisdom in that skepticism.
Alright, everyone, thanks for listening to My Weird Prompts. You can find us on Spotify and wherever you get your podcasts. If you've got a weird question or prompt you think we should explore, send it our way. And if you're interested in system prompting, fine-tuning, or the Model Context Protocol, there's never been a better time to experiment. The tools are out there, they're accessible, and the only thing stopping you is your own curiosity.
And maybe a well-crafted system prompt.
Yeah, that too. Thanks for listening, everyone. We'll catch you next time on My Weird Prompts.
