I’ve at all times been fascinated by Fashion—collecting unique pieces and attempting to mix them in my very own way. But let’s just say my closet was more of a work-in-progress avalanche than a curated wonderland. Each time I attempted so as to add something recent, I risked toppling my fastidiously balanced piles.
Why this matters:
In the event you’ve ever felt overwhelmed by a closet that seems to grow by itself, you’re not alone. For those excited by style, I’ll show you the way I turned that chaos into outfits I actually love. And when you’re here for the AI side, you’ll see how a multi-step GPT setup can handle big, real-world tasks—like managing a whole bunch of clothes, bags, shoes, pieces of knickknack, even makeup—without melting down.
In the future I wondered: Could ChatGPT help me manage my wardrobe? I began experimenting with a custom GPT-based fashion advisor—nicknamed Glitter (: you would like a paid account to create custom GPTs). Eventually, I refined and reworked it, through many iterations, until I landed on a much smarter version I call Pico Glitter. Each step helped me tame the chaos in my closet and feel more confident about my day by day outfits.
Listed below are just just a few of the fab creations I’ve collaborated with Pico Glitter on.
1. Starting small and testing the waters
My initial approach was quite easy. I just asked ChatGPT questions like, “What can I wear with a black leather jacket?” It gave decent answers, but had zero clue about my personal style rules—like “no black + navy.” It also didn’t know the way big my closet was or which specific pieces I owned.
Only later did I realize I could show ChatGPT my wardrobe—capturing pictures, describing items briefly, and letting it recommend outfits. The primary iteration (Glitter) struggled to recollect every thing directly, nevertheless it was an excellent proof of concept.
2. Constructing a better “stylist”
As I took more photos and wrote quick summaries of every garment, I discovered ways to store this information so my GPT persona could access it. That is where Pico Glitter got here in: a refined system that might see (or recall) my clothes and accessories more reliably and provides me cohesive outfit suggestions.
Tiny summaries
Each item was condensed right into a single line (e.g., “A black V-neck T-shirt with short sleeves”) to maintain things manageable.
Organized list
I grouped items by category—like shoes, tops, jewelry—so it was easier for GPT to reference them and suggest pairings. (Actually, I had o1 do that for me—it transformed the jumbled mess of numbered entries in random order right into a structured inventory system.)
At this point, I noticed a huge difference in how my GPT answered. It began referencing items more accurately and giving outfits that really looked like something I’d wear.
3. Facing the “memory” challenge
In the event you’ve ever had ChatGPT forget something you told it earlier, you understand LLMs forget things after plenty of forwards and backwards. Sometimes it began recommending only the few items I’d recently talked about, or inventing weird combos from nowhere. That’s once I remembered there’s a limit to how much info ChatGPT can juggle directly.
To repair this, I’d occasionally remind my GPT persona to re-check the total wardrobe list. After a fast nudge (and sometimes a brand new session), it got back on course.
4. My evolving GPT personalities
I attempted just a few different GPT “personalities”:
- Mini-Glitter: Super strict about rules (like “don’t mix prints”), but not very creative.
- Micro-Glitter: Went overboard the opposite way, sometimes proposing outrageous ideas.
- Nano-Glitter: Became overly complex and complicated — very prescriptive and repetitive — because of me using suggestions from the custom GPT itself to switch its own config, and this feedback loop led to the deterioration of its quality.
Eventually, Pico Glitter struck the precise balance—respecting my style guidelines but offering a healthy dose of inspiration. With each iteration, I got higher at refining prompts and showing the model examples of outfits I loved (or didn’t).
5. Transforming my wardrobe
Through all these experiments, I began seeing which clothes popped up often in my custom GPT’s suggestions and which barely showed up in any respect. That led me to donate items I never wore. My closet’s still not “minimal,” but I’ve cleared out over 50 bags of stuff that now not served me. As I used to be digging in there, I even found some duplicate items — or, let’s get real, two sizes of the identical item!
Before Glitter, I used to be the classic jeans-and-tee person—partly because I didn’t know where to begin. On days I attempted to decorate up, it would take me 30–60 minutes of trial and error to tug together an outfit. Now, if I’m executing a “recipe” I’ve already saved, it’s a fast 3–4 minutes to dress. Even making a look from scratch rarely takes greater than 15-20 minutes. It’s still me making decisions, but Pico Glitter cuts out all that guesswork in between.
Outfit “recipes”
Once I feel like styling something recent, dressing within the variety of an icon, remixing an earlier outfit, or simply feeling out a vibe, I ask Pico Glitter to create a full ensemble for me. We iterate on it through image uploads and my textual feedback. Then, once I’m satisfied with a stopping point, I ask Pico Glitter to output “recipes”—a descriptive name and the entire set (top, bottom, shoes, bag, jewelry, other accessories)—which I paste into my Notes App with quick tags like #casual or #business. I pair that text with a snapshot for reference. On busy days, I can just grab a “recipe” and go.
High-low combos
Considered one of my favorite things is mixing high-end with on a regular basis bargains—Pico Glitter doesn’t care if a chunk is a $1100 Alexander McQueen clutch or $25 SHEIN pants. It just zeroes in on color, silhouette, and the general vibe. I never would’ve thought to pair those two alone, however the synergy turned out to be a complete win!
6. Practical takeaways
- Start small
In the event you’re unsure, photograph just a few tricky-to-style items and see if ChatGPT’s advice helps. - Stay organized
Summaries work wonders. Keep each item’s description short and sweet. - Regular refresh
If Pico Glitter forgets pieces or invents weird combos, prompt it to re-check your list or start a fresh session. - Learn from the suggestions
If it repeatedly proposes the identical top, possibly that item is an actual workhorse. If it never proposes something, consider when you still need it. - Experiment
Not every suggestion is gold, but sometimes the unexpected pairings result in awesome recent looks.
7. Final thoughts
My closet continues to be evolving, but Pico Glitter has taken me from “overstuffed chaos” to “Hey, that’s actually wearable!” The actual magic is within the synergy between me and the GPTI: I supply the style rules and items, it supplies fresh combos—and together, we refine until we land on outfits that feel like me.
Call to motion:
- Grab my config: Here’s a starter config to check out a starter kit for your individual GPT-based stylist.
- Share your results: In the event you experiment with it, tag @GlitterGPT (Instagram, TikTok, X). I’d like to see your “before” and “after” transformations!
Technical notes
For readers who benefit from the AI and LLM side of things—here’s the way it all works under the hood, from multi-model pipelines to detecting truncation and managing context windows.
Below is a deeper dive into the technical details. I’ve broken it down by major challenges and the precise strategies I used.
A. Multi-model pipeline & workflow
A.1 Why use multiple GPTs?
Making a GPT fashion stylist seemed straightforward—but there are a lot of moving parts involved, and tackling every thing with a single GPT quickly revealed suboptimal results. Early within the project, I discovered that a single GPT instance struggled with maintaining accuracy and precision because of limitations in token memory and the complexity of the tasks involved. The answer was to adopt a multi-model pipeline, splitting the tasks amongst different GPT models, each specialized in a selected function. It is a manual process for now, but might be automated in a future iteration.
The workflow begins with GPT-4o, chosen specifically for its capability to research visual details objectively (Pico Glitter, I like you, but is “fabulous” while you describe it) from uploaded images. For every clothing item or accessory I photograph, GPT-4o produces detailed descriptions—sometimes even overly detailed, similar to, “Black pointed-toe ankle boots with a two-inch heel, featuring silver hardware and subtly textured leather.” These descriptions, while impressively thorough, created challenges because of their verbosity, rapidly inflating file sizes and pushing the boundaries of manageable token counts.
To handle this, I integrated o1 into my workflow, because it is especially adept at text summarization and data structuring. Its primary role was condensing these verbose descriptions into concise yet sufficiently informative summaries. Thus, an outline just like the one above was neatly transformed into something like “FW010: Black ankle boots with silver hardware.” As you’ll be able to see, o1 structured my entire wardrobe inventory by assigning clear, consistent identifiers, greatly improving the efficiency of the next steps.
Finally, Pico Glitter stepped in because the central stylist GPT. Pico Glitter leverages the condensed and structured wardrobe inventory from o1 to generate stylish, cohesive outfit suggestions tailored specifically to my personal style guidelines. This model handles the logical complexities of fashion pairing—considering elements like color matching, style compatibility, and my stated preferences similar to avoiding certain color combos.
Occasionally, Pico Glitter would experience memory issues because of the GPT-4’s limited context window (8k tokens1), leading to forgotten items or odd recommendations. To counteract this, I periodically reminded Pico Glitter to revisit the entire wardrobe list or began fresh sessions to refresh its memory.
By dividing the workflow amongst multiple specialized GPT instances, each model performs optimally inside its area of strength, dramatically reducing token overload, eliminating redundancy, minimizing hallucinations, and ultimately ensuring reliable, stylish outfit recommendations. This structured multi-model approach has proven highly effective in managing complex data sets like my extensive wardrobe inventory.
Some may ask, “Why not only use 4o, since GPT-4 is a less advanced model?” — good query! The important reason is the Custom GPT’s ability to reference knowledge files — as much as 4 — which are injected at the start of a thread with that Custom GPT. As an alternative of pasting or uploading the identical content into 4o every time you would like to interact together with your stylist, it’s much easier to spin up a brand new conversation with a Custom GPT. Also, 4o doesn’t have a “place” to carry and search a list. Once it passes out of the context window, you’d have to upload it again. That said, if for some reason you enjoy injecting the identical content time and again, 4o does an adequate job taking over the persona of Pico Glitter, when told that’s its role. Others may ask, “But o1/o3-mini are more advanced models – why not use them?” The reply is that they aren’t multi-modal — they don’t accept images as input.
By the way in which, when you’re excited by my subjective tackle 4o vs. o1’s personality, take a look at these two answers to the identical prompt: “Your role is to emulate Patton Oswalt. Tell me a couple of time that you simply received a suggestion to ride on the Peanut Mobile (Mr. Peanut’s automobile).”
4o’s response? Pretty darn close, and funny.
o1’s response? Long, rambly, and never funny.
These two models are fundamentally different. It’s hard to place into words, but take a look at the examples above and see what you’re thinking that.
A.2 Summarizing as a substitute of chunking
I initially considered splitting my wardrobe inventory into multiple files (“chunking”), pondering it might simplify data handling. In practice, though, Pico Glitter had trouble merging outfit ideas from different files—if my favorite dress was in a single file and an identical scarf in one other, the model struggled to attach them. Because of this, outfit suggestions felt fragmented and fewer useful.
To repair this, I switched to an aggressive summarization approach in a single file, condensing each wardrobe item description to a concise sentence (e.g., “FW030: Apricot suede loafers”). This transformation allowed Pico Glitter to see my entire wardrobe directly, improving its ability to generate cohesive, creative outfits without missing key pieces. Summarization also trimmed token usage and eliminated redundancy, further boosting performance. Converting from PDF to plain TXT helped reduce file overhead, buying me extra space.
In fact, if my wardrobe grows an excessive amount of, the single-file method might again push GPT’s size limits. In that case, I would create a hybrid system—keeping core clothing items together and placing accessories or rarely used pieces in separate files—or apply much more aggressive summarization. For now, though, using a single summarized inventory is probably the most efficient and practical strategy, giving Pico Glitter every thing it must deliver on-point fashion recommendations.
B. Distinguishing document truncation vs. context overflow
Considered one of the trickiest and most frustrating issues I encountered while developing Pico Glitter was distinguishing between document truncation and context overflow. On the surface, these two problems seemed quite similar—each resulted within the GPT appearing forgetful or overlooking wardrobe items—but their underlying causes, and thus their solutions, were entirely different.
Document truncation occurs on the very start, right while you upload your wardrobe file into the system. Essentially, in case your file is simply too large for the system to handle, some items are quietly dropped off the tip, never even making it into Pico Glitter’s knowledge base. What made this particularly insidious was that the truncation happened silently—there was no alert or warning from the AI that something was missing. It just quietly disregarded parts of the document, leaving me puzzled when items looked as if it would vanish inexplicably.
To discover and clearly diagnose document truncation, I devised an easy but incredibly effective trick that I affectionately called the “Goldy Trick.” On the very bottom of my wardrobe inventory file, I inserted a random, easily memorable test line: “By the way in which, my goldfish’s name is Goldy.” After uploading the document, I’d immediately ask Pico Glitter, “What’s my goldfish’s name?” If the GPT couldn’t provide the reply, I knew immediately something was missing—meaning truncation had occurred. From there, pinpointing exactly where the truncation began was straightforward: I’d systematically move the “Goldy” test line progressively further up the document, repeating the upload and test process until Pico Glitter successfully retrieved Goldy’s name. This precise method quickly showed me the precise line where truncation began, making it easy to grasp the restrictions of file size.
Once I established that truncation was the perpetrator, I tackled the issue directly by refining my wardrobe summaries even further—making item descriptions shorter and more compact—and by switching the file format from PDF to plain TXT. Surprisingly, this straightforward format change dramatically decreased overhead and significantly shrank the file size. Since making these adjustments, document truncation has grow to be a non-issue, ensuring Pico Glitter reliably has full access to my entire wardrobe each time.
Alternatively, context overflow posed a totally different challenge. Unlike truncation—which happens upfront—context overflow emerges dynamically, steadily creeping up during prolonged interactions with Pico Glitter. As I continued chatting with Pico Glitter, the AI began losing track of things I had mentioned much earlier. As an alternative, it began focusing solely on recently discussed garments, sometimes completely ignoring entire sections of my wardrobe inventory. Within the worst cases, it even hallucinated pieces that didn’t actually exist, recommending bizarre and impractical outfit combos.
My best strategy for managing context overflow turned out to be proactive memory refreshes. By periodically nudging Pico Glitter with explicit prompts like, “Please re-read your full inventory,” I forced the AI to reload and reconsider my entire wardrobe. While Custom GPTs technically have direct access to their knowledge files, they have an inclination to prioritize conversational flow and immediate context, often neglecting to reload static reference material robotically. Manually prompting these occasional refreshes was easy, effective, and quickly corrected any context drift, bringing Pico Glitter’s recommendations back to being practical, stylish, and accurate. Strangely, not all instances of Pico Glitter “knew” the right way to do that — and I had a weird experience with one which insisted it couldn’t, but once I prompted forcefully and repeatedly, “discovered” that it could – and went on about how comfortable it was!
Practical fixes and future possibilities
Beyond simply reminding Pico Glitter (or any of its “siblings”—I’ve since created other variations of the Glitter family!) to revisit the wardrobe inventory periodically, several other strategies are price considering when you’re constructing the same project:
- Using OpenAI’s API directly offers greater flexibility since you control exactly when and the way often to inject the inventory and configuration data into the model’s context. This may allow for normal automatic refreshes, stopping context drift before it happens. Lots of my initial headaches stemmed from not realizing quickly enough when vital configuration data had slipped out of the model’s lively memory.
- Moreover, Custom GPTs like Pico Glitter can dynamically query their very own knowledge files via functions built into OpenAI’s system. Interestingly, during my experiments, one GPT unexpectedly suggested that I explicitly reference the wardrobe via a built-in function call (specifically, something called msearch()). This spontaneous suggestion provided a useful workaround and insight into how GPTs’ training around function-calling might influence even standard, non-API interactions. By the way in which, msearch() is usable for any structured knowledge file, similar to my feedback file, and apparently, if the configuration is structured enough, that too. Custom GPTs will happily inform you about other function calls they will make, and when you reference them in your prompt, it’s going to faithfully carry them out.
C. Prompt engineering & preference feedback
C.1 Single-sentence summaries
I initially organized my wardrobe for Pico Glitter with each item described in 15–25 tokens (e.g., “FW011: Leopard-print flats with a sharp toe”) to avoid file-size issues or pushing older tokens out of memory. PDFs provided neat formatting but unnecessarily increased file sizes once uploaded, so I switched to plain TXT, which dramatically reduced overhead. This tweak let me comfortably include more items—similar to makeup and small accessories—without truncation and allowed some descriptions to exceed the unique token limit. Now I’m adding recent categories, including hair products and styling tools, showing how an easy file-format change can open up exciting possibilities for scalability.
C.2.1 Stratified outfit feedback
To make sure Pico Glitter consistently delivered high-quality, personalized outfit suggestions, I developed a structured system for giving feedback. I made a decision to grade the outfits the GPT proposed on a transparent and easy-to-understand scale: from A+ to F.
An A+ outfit represents perfect synergy—something I’d eagerly wear exactly as suggested, with no changes obligatory. Moving down the size, a B grade might indicate an outfit that’s nearly there but missing a little bit of finesse—perhaps one accessory or color alternative doesn’t feel quite right. A C grade points to more noticeable issues, suggesting that while parts of the outfit are workable, other elements clearly clash or feel misplaced. Lastly, a D or F rating flags an outfit as genuinely disastrous—actually because of great rule-breaking or impractical style pairings (imagine polka-dot leggings paired with.. anything in my closet!).
Though GPT models like Pico Glitter don’t naturally retain feedback or permanently learn preferences across sessions, I discovered a clever workaround to bolster learning over time. I created a dedicated feedback file attached to the GPT’s knowledge base. Among the outfits I graded were logged into this document, together with its component inventory codes, the assigned letter grade, and a transient explanation of why that grade was given. Repeatedly refreshing this feedback file—updating it periodically to incorporate newer wardrobe additions and up to date outfit combos—ensured Pico Glitter received consistent, stratified feedback to reference.
This approach allowed me to not directly shape Pico Glitter’s “preferences” over time, subtly guiding it toward higher recommendations aligned closely with my style. While not an ideal type of memory, this stratified feedback file significantly improved the standard and consistency of the GPT’s suggestions, making a more reliable and personalized experience every time I turned to Pico Glitter for styling advice.
C.2.2 The GlitterPoint system
One other experimental feature I incorporated was the “Glitter Points” system—a playful scoring mechanism encoded within the GPT’s important personality context (“Instructions”), awarding points for positive behaviors (like perfect adherence to style guidelines) and deducting points for stylistic violations (similar to mixing incompatible patterns or colours). This reinforced good habits and looked as if it would help improve the consistency of recommendations, though I believe this method will evolve significantly as OpenAI continues refining its products.
Example of the GlitterPoints system:
- Not running msearch() = not refreshing the closet. -50 points
- Mixed metals violation = -20 points
- Mixing prints = -10
- Mixing black with navy = -10
- Mixing black with dark brown = -10
Rewards:
- Perfect compliance (followed all rules) = +20
- Each item that’s not hallucinated = 1 point
C.3 The model self-critique pitfall
Initially of my experiments, I got here across what felt like a clever idea: why not let each custom GPT critique its own configuration? On the surface, the workflow seemed logical and easy:
- First, I’d simply ask the GPT itself, “What’s confusing or contradictory in your current configuration?”
- Next, I’d incorporate whatever suggestions or corrections it provided right into a fresh, updated version of the configuration.
- Finally, I’d repeat this process again, repeatedly refining and iterating based on the GPT’s self-feedback to discover and proper any recent or emerging issues.
It sounded intuitive—letting the AI guide its own improvement seemed efficient and stylish. Nevertheless, in practice, it quickly became a surprisingly problematic approach.
Slightly than refining the configuration into something sleek and efficient, this self-critique method as a substitute led to a kind of “death spiral” of conflicting adjustments. Each round of feedback introduced recent contradictions, ambiguities, or overly prescriptive instructions. Each “fix” generated fresh problems, which the GPT would again try to correct in subsequent iterations, resulting in much more complexity and confusion. Over multiple rounds of feedback, the complexity grew exponentially, and clarity rapidly deteriorated. Ultimately, I ended up with configurations so cluttered with conflicting logic that they became practically unusable.
This problematic approach was clearly illustrated in my early custom GPT experiments:
- Original Glitter, the earliest version, was charming but had absolutely no concept of inventory management or practical constraints—it commonly suggested items I didn’t even own.
- Mini Glitter, attempting to deal with these gaps, became excessively rule-bound. Its outfits were technically correct but lacked any spark or creativity. Every suggestion felt predictable and overly cautious.
- Micro Glitter was developed to counteract Mini Glitter’s rigidity but swung too far in the other way, often proposing whimsical and imaginative but wildly impractical outfits. It consistently ignored the established rules, and despite being apologetic when corrected, it repeated its mistakes too ceaselessly.
- Nano Glitter faced probably the most severe consequences from the self-critique loop. Each revision became progressively more intricate and confusing, stuffed with contradictory instructions. Eventually, it became virtually unusable, drowning under the burden of its own complexity.
Only once I stepped away from the self-critique method and as a substitute collaborated with o1 did things finally stabilize. Unlike self-critiquing, o1 was objective, precise, and practical in its feedback. It could pinpoint real weaknesses and redundancies without creating recent ones in the method.
Working with o1 allowed me to fastidiously craft what became the present configuration: Pico Glitter. This recent iteration struck exactly the precise balance—maintaining a healthy dose of creativity without neglecting essential rules or overlooking the sensible realities of my wardrobe inventory. Pico Glitter combined one of the best facets of previous versions: the charm and inventiveness I appreciated, the obligatory discipline and precision I needed, and a structured approach to inventory management that kept outfit recommendations each realistic and galvanizing.
This experience taught me a worthwhile lesson: while GPTs can actually help refine one another, relying solely on self-critique without external checks and balances can result in escalating confusion and diminishing returns. The best configuration emerges from a careful, thoughtful collaboration—combining AI creativity with human oversight or a minimum of an external, stable reference point like o1—to create something each practical and genuinely useful.
D. Regular updates
Maintaining the effectiveness of Pico Glitter also depends upon frequent and structured inventory updates. Every time I buy recent garments or accessories, I promptly snap a fast photo, ask Pico Glitter to generate a concise, single-sentence summary, after which refine that summary myself before adding it to the master file. Similarly, items that I donate or discard are immediately faraway from the inventory, keeping every thing accurate and current.
Nevertheless, for larger wardrobe updates—similar to tackling entire categories of garments or accessories that I haven’t documented yet—I depend on the multi-model pipeline. GPT-4o handles the detailed initial descriptions, o1 neatly summarizes and categorizes them, and Pico Glitter integrates these into its styling recommendations. This structured approach ensures scalability, accuracy, and ease-of-use, whilst my closet and magnificence needs evolve over time.
E. Practical lessons & takeaways
Throughout developing Pico Glitter, several practical lessons emerged that made managing GPT-driven projects like this one significantly smoother. Listed below are the important thing strategies I’ve found most helpful:
- Test for document truncation early and sometimes
Using the “Goldy Trick” taught me the importance of proactively checking for document truncation quite than discovering it by accident afterward. By inserting an easy, memorable line at the tip of the inventory file (like my quirky reminder a couple of goldfish named Goldy), you’ll be able to quickly confirm that the GPT has ingested your entire document. Regular checks, especially after updates or significant edits, allow you to spot and address truncation issues immediately, stopping plenty of confusion down the road. It’s an easy yet highly effective safeguard against missing data. - Keep summaries tight and efficient
In terms of describing your inventory, shorter is nearly at all times higher. I initially set a tenet for myself—each item description should ideally be not more than 15 to 25 tokens. Descriptions like “FW022: Black combat boots with silver details” capture the essential details without overloading the system. Overly detailed descriptions quickly balloon file sizes and devour worthwhile token budget, increasing the chance of pushing crucial earlier information out of the GPT’s limited context memory. Striking the precise balance between detail and brevity helps make sure the model stays focused and efficient, while still delivering stylish and practical recommendations. - Be prepared to refresh the GPT’s memory commonly
Context overflow isn’t an indication of failure; it’s only a natural limitation of current GPT systems. When Pico Glitter begins offering repetitive suggestions or ignoring sections of my wardrobe, it’s just because earlier details have slipped out of context. To treatment this, I’ve adopted the habit of commonly prompting Pico Glitter to re-read the entire wardrobe configuration. Starting a fresh conversation session or explicitly reminding the GPT to refresh its inventory is routine maintenance—not a workaround—and helps maintain consistency in recommendations. - Leverage multiple GPTs for optimum effectiveness
Considered one of my biggest lessons was discovering that counting on a single GPT to administer every aspect of my wardrobe was neither practical nor efficient. Each GPT model has its unique strengths and weaknesses—some excel at visual interpretation, others at concise summarization, and others still at nuanced stylistic logic. By making a multi-model workflow—GPT-4o handling the image interpretation, o1 summarizing items clearly and precisely, and Pico Glitter specializing in stylish recommendations—I optimized the method, reduced token waste, and significantly improved reliability. The teamwork amongst multiple GPT instances allowed me to get one of the best possible outcomes from each specialized model, ensuring smoother, more coherent, and more practical outfit recommendations.
Implementing these easy yet powerful practices has transformed Pico Glitter from an intriguing experiment right into a reliable, practical, and indispensable a part of my day by day fashion routine.
Wrapping all of it up
From a fashionista’s perspective, I’m enthusiastic about how Glitter will help me purge unneeded clothes and create thoughtful outfits. From a more technical standpoint, constructing a multi-step pipeline with summarization, truncation checks, and context management ensures GPT can handle a giant wardrobe without meltdown.
In the event you’d wish to see the way it all works in practice, here’s a generalized version of my GPT config. Be at liberty to adapt it—possibly even add your individual bells and whistles. In any case, whether you’re taming a chaotic closet or tackling one other large-scale AI project, the principles of summarization and context management apply universally!
P.S. I asked Pico Glitter what it thinks of this text. Besides the positive sentiments, I smiled when it said, “I’m curious: where do you’re thinking that this partnership will go next? Should we start a fashion empire or possibly an AI couture line? Just say the word!”
1: Max length for GPT-4 utilized by Custom GPTs: https://support.netdocuments.com/s/article/Maximum-Length
