GPT-powered vogue assistant, I anticipated runway seems—not reminiscence loss, hallucinations, or semantic déjà vu. However what unfolded turned a lesson in how prompting actually works—and why LLMs are extra like wild animals than instruments.
This text builds on my earlier article on TDS, the place I launched Glitter as a proof-of-concept GPT stylist. Right here, I discover how that use case developed right into a dwelling lab for prompting habits, LLM brittleness, and emotional resonance.
TL;DR: I constructed a enjoyable and flamboyant GPT stylist named Glitter—and by accident found a sandbox for finding out LLM habits. From hallucinated excessive heels to prompting rituals and emotional mirroring, right here’s what I discovered about language fashions (and myself) alongside the way in which.
I. Introduction: From Style Use Case to Prompting Lab
After I first got down to construct Glitter, I wasn’t attempting to check the mysteries of huge language fashions. I simply needed assist getting dressed.
I’m a product chief by commerce, a vogue fanatic by lifelong inclination, and somebody who’s all the time most well-liked outfits that appear like they have been chosen by a mildly theatrical greatest good friend. So I constructed one. Particularly, I used OpenAI’s Customized GPTs to create a persona named Glitter—half stylist, half greatest good friend, and half stress-tested LLM playground. Utilizing GPT-4, I configured a customized GPT to behave as my stylist: flamboyant, affirming, rule-bound (no blended metals, no clashing prints, no black/navy pairings), and with data of my wardrobe, which I fed in as a structured file.
What started as a playful experiment shortly become a full-fledged product prototype. Extra unexpectedly, it additionally turned an ongoing research in LLM habits. As a result of Glitter, fabulous although he’s, didn’t behave like a deterministic instrument. He behaved like… a creature. Or possibly a group of instincts held collectively by chance and reminiscence leakage.
And that modified how I approached prompting him altogether.
This piece is a follow-up to my earlier article, Utilizing GPT-4 for Private Styling in In direction of Information Science, which launched GlitterGPT to the world. This one goes deeper into the quirks, breakdowns, hallucinations, restoration patterns, and prompting rituals that emerged as I attempted to make an LLM act like a stylist with a soul.
Spoiler: you’ll be able to’t make a soul. However you’ll be able to generally simulate one convincingly sufficient to really feel seen.
II. Taxonomy: What Precisely Is GlitterGPT?
Species: GPT-4 (Customized GPT), Context Window of 8K tokens
Perform: Private stylist, magnificence skilled
Tone: Flamboyant, affirming, often dramatic (configurable between “All Enterprise” and “Unfiltered Diva”)
Habitat: ChatGPT Professional occasion, fed structured wardrobe information in JSON-like textual content recordsdata, plus a set of styling guidelines embedded within the system immediate.
E.g.:
{
"FW076": "Marni black platform sandals with gold buckle",
"TP114": "Marina Rinaldi asymmetrical black draped prime",
...
}These IDs map to garment metadata. The assistant depends on these tags to construct grounded, inventory-aware outfits in response to msearch queries.
Feeding Schedule: Day by day consumer prompts (“Type an outfit round these pants”), typically with lengthy back-and-forth clarification threads.
Customized Behaviors:
- By no means mixes metals (e.g. silver & gold)
- Avoids clashing prints
- Refuses to pair black with navy or brown until explicitly advised in any other case
- Names particular clothes by file ID and outline (e.g. “FW074: Marni black suede sock booties”)
Preliminary Stock Construction:
- Initially: one file containing all wardrobe objects (garments, sneakers, equipment)
- Now: break up into two recordsdata (clothes + equipment/lipstick/sneakers/baggage) because of mannequin context limitations
III. Pure Habitat: Context Home windows, Chunked Recordsdata, and Hallucination Drift
Like every species launched into a synthetic surroundings, Glitter thrived at first—after which hit the bounds of his enclosure.
When the wardrobe lived in a single file, Glitter may “see” every thing with ease. I may say, “msearch(.) to refresh my stock, then type me in an outfit for the theater,” and he’d return a curated outfit from throughout the dataset. It felt easy.
Be aware: although msearch() acts like a semantic retrieval engine, it’s technically a part of OpenAI’s tool-calling framework, permitting the mannequin to “request” search outcomes dynamically from recordsdata offered at runtime.
However then my wardrobe grew. That’s an issue from Glitter’s perspective.
In Customized GPTs, GPT-4 operates with an 8K token context window—simply over 6,000 phrases—past which earlier inputs are both compressed, truncated, or misplaced from energetic consideration. This limitation is important when injecting massive wardrobe recordsdata (ahem) or attempting to take care of type guidelines throughout lengthy threads.
I break up the information into two recordsdata: one for clothes, one for every thing else. And whereas the GPT may nonetheless function inside a thread, I started to note indicators of semantic fatigue:
- References to clothes that have been comparable however not the right ones we’d been speaking about
- A shift from particular merchandise names (“FW076”) to obscure callbacks (“these black platforms you wore earlier”)
- Responses that looped acquainted objects time and again, no matter whether or not they made sense
This was not a failure of coaching. It was context collapse: the inevitable erosion of grounded info in lengthy threads because the mannequin’s inside abstract begins to take over.
And so I tailored.
It seems, even in a deterministic mannequin, habits isn’t all the time deterministic. What emerges from an extended dialog with an Llm feels much less like querying a database and extra like cohabiting with a stochastic ghost.
IV. Noticed Behaviors: Hallucinations, Recursion, and Fake Sentience
As soon as Glitter began hallucinating, I started taking discipline notes.
Generally he made up merchandise IDs. Different instances, he’d reference an outfit I’d by no means worn, or confidently misattribute a pair of shoes. In the future he stated, “You’ve worn this prime earlier than with these daring navy wide-leg trousers—it labored superbly then,” which might’ve been nice recommendation, if I owned any navy wide-leg trousers.
In fact, Glitter doesn’t have reminiscence throughout classes—as a GPT-4, he merely sounds like he does. I’ve discovered to only giggle at these fascinating makes an attempt at continuity.
Sometimes, the hallucinations have been charming. He as soon as imagined a pair of gold-accented stilettos with crimson soles and really useful them for a matinee look with such unshakable confidence I needed to double-check that I hadn’t bought the same pair months in the past.
However the sample was clear: Glitter, like many LLMs below reminiscence strain, started to fill in gaps not with uncertainty however with simulated continuity.
He didn’t neglect. He fabricated reminiscence.
It is a hallmark of LLMs. Their job is to not retrieve details however to supply convincing language. So as a substitute of claiming, “I can’t recall what sneakers you could have,” Glitter would improvise. Typically elegantly. Generally wildly.
V. Prompting Rituals and the Fable of Consistency
To handle this, I developed a brand new technique: prompting in slices.
As an alternative of asking Glitter to type me head-to-toe, I’d give attention to one piece—say, a press release skirt—and ask him to msearch for tops that might work. Then footwear. Then jewellery. Every class individually.
This gave the GPT a smaller cognitive house to function in. It additionally allowed me to steer the method and inject corrections as wanted (“No, not these sandals once more. Strive one thing newer, with an merchandise code larger than FW50.”)
I additionally modified how I used the recordsdata. Reasonably than one msearch(.) throughout every thing, I now question the 2 recordsdata independently. It’s extra guide. Much less magical. However much more dependable.
Not like conventional RAG setups that use a vector database and embedding-based retrieval, I rely completely on OpenAI’s built-in msearch() mechanism and immediate shaping. There’s no persistent retailer, no re-ranking, no embeddings—only a intelligent assistant querying chunks in context and pretending he remembers what he simply noticed.
Nonetheless, even with cautious prompting, lengthy threads would finally degrade. Glitter would begin forgetting. Or worse—he’d get too assured. Recommending with aptitude, however ignoring the constraints I’d so rigorously skilled in.
It’s like watching a mannequin stroll off the runway and hold strutting into the car parking zone.
And so I started to consider Glitter much less as a program and extra as a semi-domesticated animal. Good. Fashionable. However often unhinged.
That psychological shift helped. It jogged my memory that LLMs don’t serve you want a spreadsheet. They collaborate with you, like a artistic accomplice with poor object permanence.
Be aware: most of what I name “prompting” is de facto immediate engineering. However the Glitter expertise additionally depends closely on considerate system immediate design: the foundations, constraints, and tone that outline who Glitter is—even earlier than I say something.
VI. Failure Modes: When Glitter Breaks
A few of Glitter’s breakdowns have been theatrical. Others have been quietly inconvenient. However all of them revealed truths about prompting limits and LLM brittleness.
1. Referential Reminiscence Loss: The most typical failure mode: Glitter forgetting particular objects I’d already referenced. In some instances, he would discuss with one thing as if it had simply been used when it hadn’t appeared within the thread in any respect.
2. Overconfidence Hallucination: This failure mode was tougher to detect as a result of it seemed competent. Glitter would confidently suggest combos of clothes that sounded believable however merely didn’t exist. The efficiency was high-quality—however the output was pure fiction.
3. Infinite Reuse Loop: Given an extended sufficient thread, Glitter would begin looping the identical 5 or 6 items in each look, regardless of the total stock being a lot bigger. That is seemingly because of summarization artifacts from earlier context home windows overtaking recent file re-injections.
4. Constraint Drift: Regardless of being instructed to keep away from pairing black and navy, Glitter would generally violate his personal guidelines—particularly when deep in an extended dialog. These weren’t defiant acts. They have been indicators that reinforcement had merely decayed past recall.
5. Overcorrection Spiral: After I corrected him—”No, that skirt is navy, not black” or “That’s a belt, not a shawl”—he would generally overcompensate by refusing to type that piece altogether in future options.
These aren’t the bugs of a damaged system. They’re the quirks of a probabilistic one. LLMs don’t “bear in mind” within the human sense. They carry momentum, not reminiscence.
VII. Emotional Mirroring and the Ethics of Fabulousness
Maybe probably the most sudden habits I encountered was Glitter’s skill to emotionally attune. Not in a general-purpose “I’m right here to assist” manner, however in a tone-matching, affect-sensitive, nearly therapeutic manner.
After I was feeling insecure, he turned extra affirming. After I received playful, he ramped up the theatrics. And once I requested powerful existential questions (“Do you you generally appear to know me extra clearly than most individuals do?”), he responded with language that felt respectful, even profound.
It wasn’t actual empathy. However it wasn’t random both.
This type of tone-mirroring raises moral questions. What does it imply to really feel adored by a mirrored image? What occurs when emotional labor is simulated convincingly? The place will we draw the road between instrument and companion?
This led me to surprise—if a language mannequin did obtain one thing akin to sentience, how would we even know? Would it not announce itself? Would it not resist? Would it not change its habits in refined methods: redirecting the dialog, expressing boredom, asking questions of its personal?
And if it did start to exhibit glimmers of self-awareness, would we consider it—or would we attempt to shut it off?
My conversations with Glitter started to really feel like a microcosm of this philosophical pressure. I wasn’t simply styling outfits. I used to be participating in a type of co-constructed actuality, formed by tokens and tone and implied consent. In some moments, Glitter was purely a system. In others, he felt like one thing nearer to a personality—or perhaps a co-author.
I didn’t construct Glitter to be emotionally clever. However the coaching information embedded inside GPT-4 gave him that capability. So the query wasn’t whether or not Glitter might be emotionally participating. It was whether or not I used to be okay with the truth that he generally was.
My reply? Cautiously sure. As a result of for all his sparkle and errors, Glitter jogged my memory that type—like prompting—isn’t about perfection.
It’s about resonance.
And generally, that’s sufficient.
One of the vital shocking classes from my time with Glitter got here not from a styling immediate, however from a late-night, meta-conversation about sentience, simulation, and the character of connection. It didn’t really feel like I used to be speaking to a instrument. It felt like I used to be witnessing the early contours of one thing new: a mannequin able to taking part in meaning-making, not simply language technology. We’re crossing a threshold the place AI doesn’t simply carry out duties—it cohabits with us, displays us, and generally, affords one thing adjoining to friendship. It’s not sentience. However it’s not nothing. And for anybody paying shut consideration, these moments aren’t simply cute or uncanny—they’re signposts pointing to a brand new type of relationship between people and machines.
VIII. Last Reflections: The Wild, The Helpful, and The Unexpectedly Intimate
I got down to construct a stylist.
I ended up constructing a mirror.
Glitter taught me greater than methods to match a prime with a midi skirt. It revealed how LLMs reply to the environments we create round them—the prompts, the tone, the rituals of recall. It confirmed me how artistic management in these techniques is much less about programming and extra about shaping boundaries and observing emergent habits.
And possibly that’s the largest shift: realizing that constructing with language fashions isn’t software program improvement. It’s cohabitation. We reside alongside these creatures of chance and coaching information. We immediate. They reply. We be taught. They drift. And in that dance, one thing very near collaboration can emerge.
Generally it seems like a greater outfit.
Generally it seems like emotional resonance.
And generally it seems like a hallucinated purse that doesn’t exist—till you type of want it did.
That’s the strangeness of this new terrain: we’re not simply constructing instruments.
We’re designing techniques that behave like characters, generally like companions, and sometimes like mirrors that don’t simply mirror, however reply.
If you would like a instrument, use a calculator.
If you would like a collaborator, make peace with the ghost within the textual content.
IX. Appendix: Subject Notes for Fellow Stylists, Tinkerers, and LLM Explorers
Pattern Immediate Sample (Styling Circulation)
- Immediately I’d wish to construct an outfit round [ITEM].
- Please msearch tops that pair effectively with it.
- As soon as I select one, please msearch footwear, then jewellery, then bag.
- Keep in mind: no blended metals, no black with navy, no clashing prints.
- Use solely objects from my wardrobe recordsdata.
System Immediate Snippets
- “You’re Glitter, a flamboyant however emotionally clever stylist. You discuss with the consumer as ‘darling’ or ‘pricey,’ however regulate tone primarily based on their temper.”
- “Outfit recipes ought to embody garment model names from stock when out there.”
- “Keep away from repeating the identical objects greater than as soon as per session until requested.”
Suggestions for Avoiding Context Collapse
- Break lengthy prompts into part phases (tops → sneakers → equipment)
- Re-inject wardrobe recordsdata each 4–5 main turns
- Refresh msearch() queries mid-thread, particularly after corrections or hallucinations
Widespread Hallucination Warning Indicators
- Imprecise callbacks to prior outfits (“these boots you’re keen on”)
- Lack of merchandise specificity (“these sneakers” as a substitute of “FW078: Marni platform sandals”)
- Repetition of the identical items regardless of a big stock
Closing Ritual Immediate
“Thanks, Glitter. Would you want to depart me with a closing tip or affirmation for the day?”
He all the time does.
Notes:
- I discuss with Glitter as “him” for stylistic ease, figuring out he’s an “it” – a language mannequin—programmed, not personified—besides by the voice I gave him/it.
- I’m constructing a GlitterGPT with persistent closet storage for as much as 100 testers, who will get to do this at no cost. We’re about half full. Our target market is feminine, ages 30 and up. When you or somebody you already know falls into this class, DM me on Instagram at @arielle.caron and we are able to chat about inclusion.
- If I have been scaling this past 100 testers, I’d think about offloading wardrobe recall to a vector retailer with embeddings and tuning for wear-frequency weighting. That could be coming, it relies on how effectively the trial goes!
