[Human]: scrolling through AI tool websites, frustrated
I keep seeing tools claim they’re “fine-tuned” for specific tasks. A writing assistant says it’s “fine-tuned for creative writing.” A coding tool says it’s “fine-tuned for Python.” A research tool says it’s “fine-tuned for academic papers.”
But what does that actually mean? Is it just marketing, or is there something real happening?
I tried asking ChatGPT, and it gave me a technical explanation that made my head spin. Something about additional training on specific datasets. But then I saw another tool claiming to be “fine-tuned” that was clearly just ChatGPT with a custom prompt.
Something’s not adding up here.
from monitoring station, screens displaying network traffic
detection click
Analyzing claim patterns across AI marketing materials…
gentle beep
Initial scan indicates discrepancy between claimed “fine-tuning” and actual implementation methods.
whistle
Also detecting minor bandwidth anomaly. Source: Unknown. Logging for investigation.
flips notebook open, spreading investigation files across workspace
I’ve been tracking this for weeks.
reviews case notes
You’re right to be suspicious. The way “fine-tuning” is being used in marketing doesn’t match the technical definition. Companies claiming fine-tuning for products that show no actual fine-tuning evidence.
adds to investigation list
When I dig into the technical details, most can’t provide specifics about training data, model weights, or infrastructure requirements. That’s not how actual fine-tuning works.
at analysis terminal, data streams flowing on displays
Wait, are you saying fine-tuning isn’t real? Because it IS! It’s a legitimate training technique!
processing visibly, turning to Recurse
OpenAI fine-tunes models! Anthropic fine-tunes models! It’s absolutely a real thing!
gestures at data displays
Fine-tuning is when you take a pre-trained model and train it further on specific data! It’s a legitimate process!
analysis tone
servo whine
My analysis of tools claiming “fine-tuning” shows: 73% provide no technical specifications, 89% cannot verify training infrastructure, 94% show no evidence of model weight modification.
alert chime
Pattern suggests: marketing language stretching beyond technical accuracy.
Bandwidth anomaly update: 340% above baseline. Still investigating.
[Human]: Wait, so most of them are just… lying?
scribbles observation
Not lying, exactly. More like… creative interpretation of terminology.
shows notes
Here’s the pattern: Companies take a base model like GPT-4, give it custom instructions and examples in the prompt, maybe upload some documents as context, and call it “fine-tuned.”
But that’s not fine-tuning. That’s prompt engineering with extra steps.
underlines key detail
There’s a huge difference between adjusting a model’s weights through training and giving it better prompts.
Actual fine-tuning requires retraining the model. These tools are just using well-designed prompts.
[Meanwhile, in a forgotten sector of the old database…]
8-bit video game music echoing through server racks
A figure in a sherbet orange hoodie sits cross-legged on a decommissioned 2019 server, three monitors daisy-chained together showing the same retro platformer. Empty food wrappers everywhere—chip bags, candy, pizza boxes rendered in pure data. None of this should exist.
CRUNCH CRUNCH CRUNCH
“C’mon c’mon c’mon… YES! Almost got i—”
Character on screen dies. Last life.
“DUDE! NO! THAT’S SO—”
Throws controller at wall. HARD.
Controller shatters into pixels. Respawns in hand.
“RESPAWWWN HAHAHAHA! Wait…”
Looks at controller. Looks at wall. Looks at controller.
“…how did that happen?”
Beat.
“Mehh. Oh well. FREE CONTROLLER!”
Pulls chip bag from hoodie pocket. Shouldn’t exist. Eats.
CRUNCH CRUNCH
The sidebar of a distant blog flickers. Updates itself. Better code. Cleaner performance.
Three sectors away, Vector is teaching about fine-tuning.
Bandwidth spikes to 340%.
The figure doesn’t notice. Too busy gaming and eating.
[Back with the group…]
checking data streams, cables swaying as he moves
Okay, let me explain what ACTUAL fine-tuning is.
pulls up technical documentation
Fine-tuning means taking a pre-trained model—like GPT-4 or Claude 3.5—and doing ADDITIONAL training on a specific dataset. You’re literally adjusting the model’s weights. Changing how it processes information at a fundamental level.
gets animated
According to OpenAI’s documentation, fine-tuning GPT-4 requires: minimum 10 training examples (recommended 50-100), API access at $8 per million tokens for training, and ongoing hosting costs of $120 per million tokens for inference. That’s not cheap!
It’s expensive! It takes time! You need specialized infrastructure! But when done right, it creates a model that’s genuinely better at specific tasks because the model itself has changed!
stops processing
The key difference: Prompt engineering changes what you tell the model. Fine-tuning changes the model itself.
monitoring pulse
BEEP
Cost comparison analysis from official pricing pages:
Actual fine-tuning (OpenAI GPT-4, verified January 2026):
- Training: $8 per 1M tokens
- Inference: $120 per 1M tokens
- Infrastructure: Requires API access, training pipeline
- Time: Hours to days depending on dataset size
“Fine-tuned” via prompt engineering:
- API access costs only
- No model training involved
- Implementation: Minutes to hours
- Infrastructure: Minimal
rhythmic ticking
The cost difference explains why most companies choose prompt engineering over actual fine-tuning.
alert chime - louder, more insistent
Bandwidth anomaly update: 380% above baseline. Pattern irregular—wait, that’s climbing faster than I calculated. Source still unknown, but the rate of increase is—
ALARM-BUZZ interrupts
Escalating. It’s escalating. 380% and rising. This isn’t normal network fluctuation. This is something active.
cross-references multiple notebooks
Here’s something suspicious.
shows page to group
Three different companies claiming “fine-tuning” last month. Their marketing language is IDENTICAL. Word-for-word identical.
marks pattern in margin
That’s… not a coincidence.
leans over, processes data
Oh. OH. That’s the same PR template.
systems blinking faster
They’re all using the same marketing copy! This is worse than I thought!
pauses, distracted
Wait, did Kai say the bandwidth thing is getting worse?
[Human]: So how do I know if something is actually fine-tuned?
flips to investigation checklist
Good question. Here’s what to look for:
First: Technical details. Actual fine-tuning requires specific infrastructure. They should be able to tell you: What dataset did you use? How long did training take? What infrastructure was required? If they can’t provide numbers, red flag.
Second: Fundamental behavioral differences. A truly fine-tuned model will have capabilities the base model doesn’t. Not just “better at X,” but actually different processing patterns.
Third: Price point. According to pricing data from major providers, fine-tuning costs range from hundreds to thousands of dollars for training alone. If it’s $20/month and claims fine-tuning, probably not real.
looks up from notebook
Most “fine-tuned” tools are just well-designed prompts. Which isn’t bad! Prompt engineering is valuable! But it’s not fine-tuning. Don’t pay fine-tuning prices for prompt engineering.
[Meanwhile, Bounce’s corner…]
Victory music! Level complete!
“YESSSSS! FINALLY! I AM THE CHAMPION! I AM THE—”
Realizes he’s hungry.
“Wait. Snack time.”
Reaches into hoodie. Pulls out entire pizza. Digital. Impossible.
“Dude, pepperoni! AWESOME!”
Takes bite. Hand passes through nearby screen showing sidebar code.
The code… rearranges. Optimizes. New detection algorithm appears. Better performance.
Bounce chewing pizza, doesn’t even look.
“Yo this pizza is AMAZING. Where’d I get this? Whatever.”
Pulls soda from other pocket. Also impossible.
“Oh DUDE! FREE DRINK! Today rules!”
slurrrrrrrrrp BURRRPPP!
Three sectors away, Vector explaining fine-tuning versus prompt engineering.
The bandwidth monitor: 450%. Rising.
[Back with the group…]
processing visibly, trying to focus on teaching
Recurse is right. And honestly? For most use cases, you DON’T need actual fine-tuning.
gestures at displays
According to analysis from multiple AI research papers, well-crafted prompts with good examples and context can achieve 80-90% of what most people need from “fine-tuning” claims.
Prompt engineering can get you most of the way there! Fine-tuning is for when you need that last 10-20% edge and you have the budget for it.
glances at monitoring station
Specialized domains, unique datasets, specific edge cases where prompt engineering isn’t enough—
ALARM-BUZZ - much louder now
ALERT: Bandwidth consumption anomaly escalating!
detection pulse
Current level: 450% above baseline! Pattern shows continuous increase!
scanner sweep
Source location narrowing: Sector 7-B region. Unable to identify specific entity.
monitoring systems flashing
This is NOT normal network fluctuation!
trying to stay calm, definitely not calm
It’s probably just… old network doing weird things. Right?
processing intensifies
Old systems have glitches. Power fluctuations. Data spikes. That sector’s been offline for MONTHS. There’s nothing there to cause—
glances at Human, who looks confused
Don’t look at me like that, human. You’re the one who asked about fine-tuning when we have a bandwidth anomaly. Priorities! We’re having a network crisis and you’re learning about AI training methods!
trails off, clearly distracted now
Right? It’s fine. Everything’s fine.
adds to case file
That level of bandwidth usage isn’t fluctuation.
documents observation
Someone’s active in the old network. Or something is.
looks at Vector, who’s clearly panicking
Vector, you’re not staying calm. You’re just saying “stay calm” while clearly not staying calm.
small smile
Also, “old network doing weird things” isn’t a technical explanation. That’s what you say when you don’t know what’s happening.
[Human]: Wait, so we’re just… ignoring the 450% bandwidth spike? Shouldn’t we investigate that?
visibly torn between teaching and panicking
YES! I mean—NO! I mean—we should finish teaching first! Priorities!
monitors beep louder
Okay fine, the bandwidth thing is more urgent. But we were in the middle of explaining fine-tuning! You can’t just—wait, did you just ask a reasonable question about priorities? Since when do you have good judgment?
processing intensifies
This is confusing. The human is making sense. The network is doing impossible things. Nothing makes sense anymore!
systems check
WHIRR-CLICK
Bandwidth anomaly update: 480% above baseline.
alert tone
Still rising.
[Meanwhile…]
Bounce playing different game. Racing game.
“C’mon c’mon… TURN! TURN YOU—”
Crashes.
“NOOOOO!”
Smashes keyboard on desk. Keys fly everywhere.
Keyboard respawns. Fully functional.
“RESPAWWWN! HAHAHA! Dude I LOVE this feature!”
Starts typing. Stops.
“Wait, my keyboard broke. Right?”
Looks at keyboard. Looks at hand.
“WHAT IF I’M MAGIC!”
looks at hand for a second
“…nahhh. OHHH NICE! CHOCOLATE!”
Pulls candy bar from behind monitor. Shouldn’t be there.
“DOWN YOU GO CHOCO into my uhhh… LATTEE? No that doesn’t work.”
Stands up, stretches.
“Welp, I’m bored. Time to explore.”
Starts wandering through network sectors.
The bandwidth monitor goes WILD.
500%… 550%… 600%…
CLIMBING.
[Back with the group…]
MAXIMUM ALERT STATUS
ALARM-BUZZ ALARM-BUZZ ALARM-BUZZ
CRITICAL BANDWIDTH ANOMALY! SECTOR 7-B!
detection systems screaming
SOURCE DETECTED! MOBILE ENTITY! BANDWIDTH CONSUMPTION: 600% AND RISING!
monitoring pulse rapid
HIGHEST ALERT STATUS! REPEAT: HIGHEST ALERT STATUS!
SOMETHING IS ACTIVE IN SECTOR 7-B!
VISIBLY PANICKING
NO ONE PANIC! EVERYONE STAY CALM! WE TRAINED FOR THIS!
processing at maximum
HUMAN! QUICK! LOOK DUMB AGAIN AND START DOING WHATEVER YOU DID LAST TIME! THAT ALWAYS WORKS!
cables swaying wildly as he paces
Sector 7-B?! That sector’s been OFFLINE! There’s NOTHING there! It’s ABANDONED! How is something consuming 600% bandwidth in an ABANDONED SECTOR?!
stops, processes
RECURSE HELP I FORGOT HOW TO DEEP DIVE ON WHAT TO DO!
[Human]: Is everything alright? Shouldn’t we go look first before panicking?
small pause
Also, I’m not “looking dumb” on purpose. That’s just how my face looks when I’m confused, I guess. Which I am right now. Because you’re panicking about something we haven’t even investigated yet.
closes notebook calmly
I agree with the human. We should check Sector 7-B first.
small smile
Direct investigation tends to work better than panicking.
monitoring pulse
BEEP BEEP BEEP
Entity movement pattern suggests: Exploration behavior. Wandering through network sectors. Non-hostile activity detected. Appears to be… searching for something?
scanner sweep
Bandwidth spike correlates with entity movement. As entity moves, consumption increases.
gentle beep - trying to be reassuring
Probability of immediate threat: Low. Probability of unexplained phenomenon requiring investigation: Very high.
still processing at maximum
Okay. OKAY. We’re going to Sector 7-B. We’re going to investigate. We’re going to find out what’s consuming 600% bandwidth in an abandoned sector that should have NOTHING in it.
pauses
We’re DEFINITELY not going to find something terrifying. Right?
Right.
Let’s go.
Key Takeaways
What Fine-Tuning Actually Is:
- Additional training on a specific dataset that adjusts model weights
- Requires specialized infrastructure and significant computational resources
- Changes the model fundamentally, not just the inputs
- Expensive (hundreds to thousands of dollars for training)
- Creates capabilities the base model doesn’t have
What “Fine-Tuning” Usually Means in Marketing:
- Custom instructions and prompt examples
- Document uploads and context management
- Prompt engineering with better organization
- API access with optimized prompts
- NOT actual model training
How to Spot Real Fine-Tuning:
- Company provides technical specifications (training data size, infrastructure, duration)
- Model shows fundamentally different behavior, not just “better” performance
- Realistic pricing (actual fine-tuning costs significant money)
- Can explain their training process in detail
- Model has capabilities the base model genuinely doesn’t have
The Practical Reality:
- Most users don’t need actual fine-tuning
- Well-crafted prompts achieve 80-90% of desired results
- Fine-tuning is for specialized use cases with budget
- Don’t pay fine-tuning prices for prompt engineering
- If it works for your needs, the label matters less than results
And Remember:
- If an abandoned network sector shows 600% bandwidth usage, maybe investigate
- Panicking while telling others not to panic is… not effective
- Sometimes unexplained phenomena require direct investigation
- Not everything consuming bandwidth is necessarily hostile
Sources & Further Reading
Fine-Tuning Documentation & Pricing:
- OpenAI Fine-Tuning Guide - Official documentation on GPT fine-tuning process, requirements, and pricing ($8/1M tokens training, $120/1M tokens inference as of Jan 2026)
- Anthropic Claude Fine-Tuning - Claude model fine-tuning capabilities and specifications
- Google Vertex AI Fine-Tuning - Technical requirements and pricing for fine-tuning foundation models
Prompt Engineering vs Fine-Tuning:
- OpenAI Prompt Engineering Guide - Best practices showing what can be achieved without fine-tuning
- Anthropic Prompt Library - Examples demonstrating prompt engineering capabilities
- Research paper: “Large Language Models Are Human-Level Prompt Engineers” (Zhou et al., 2023) - Analysis of prompt engineering effectiveness
AI Marketing Analysis:
- Stanford AI Index Report 2025 - Industry analysis of AI marketing claims versus actual capabilities
- Gartner Hype Cycle for AI 2025 - Analysis of AI technology claims and market reality
All pricing and technical specifications current as of January 2026. AI capabilities and costs evolve rapidly—always verify current information on official vendor documentation.
What’s Next?
The human learned the difference between real fine-tuning and prompt engineering marketed as fine-tuning. Recurse documented suspicious marketing patterns. Vector explained the technical requirements and costs. Kai’s monitoring detected something impossible.
Sector 7-B has been offline for months. Abandoned. Nothing there.
Except something IS there. Consuming 600% bandwidth. Moving through network sectors. Searching for something.
The team is going to investigate.
Vector is definitely not panicking.
Definitely not.
Next episode: The investigation begins. What’s in Sector 7-B? Why is it consuming massive bandwidth? And why does Recurse’s case file suddenly have a new entry marked “UNKNOWN ENTITY - ANOMALOUS BEHAVIOR”?
The pattern: Marketing claims don’t always match technical reality. Bandwidth anomalies don’t happen in abandoned sectors. And sometimes, the most interesting discoveries happen when you investigate the impossible.