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Improving AI generated content with simplified data models.
Jul 15, 2024
Adding smarts by getting stupid.
I never felt the AI magic more than the first time an LLM spit out a blob of JSON that represented a Simily simulation. Something from nothing. Magic! Unfortunately, upon closer inspection, the output was trash!
But that makes sense, a Simily simulation has a lot going on:
When asked to generate a “Carbon footprint of an average home in Minnesota” it needs to consider what a household’s primary carbon sources are, through what means they produce CO2 and in what quantities over what time periods. It needs to have a basic understanding of the entire system so it can determine how resources flow from one part of the system to the others. It needs to decide resource flow priorities, and when and how to apply basic math. It’s a lot.
Here is the export of the model pictured above:
{
"formatVersion": 2,
"generationTime": "2024-01-31T15:40:49.429",
"name": "Carbon Footprint",
"targetModelPath": "/models/1/simulations/f418c296-27b0-472e-afc5-28868c3dd6a8/versions/a52a13d2-27b5-46a8-8a7c-5b07370b4720",
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"visibility": "public",
"name": "Carbon Footprint",
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"/models/1/formulas/ba3fba7b-7f64-4dae-82a5-c014409eea4a/versions/a52a13d2-27b5-46a8-8a7c-5b07370b4720": {
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"ref": "a52a13d2-27b5-46a8-8a7c-5b07370b4720"
},
"visibility": "public",
"name": "Natural Gas",
"decoration": {
"image": {
"id": "574af609-e0e6-4c05-bb06-54900e4d4138",
"type": "web",
"icon": null,
"web": {
"url": "https://images.unsplash.com/photo-1622801179563-94f5aa9a1c6d?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w0NTA0Mzl8MHwxfHJhbmRvbXx8fHx8fHx8fDE2ODU3MjM4ODJ8&ixlib=rb-4.0.3&q=80&w=1080",
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}
},
"colorHex": "#ff9e9e9e"
},
"timeSpan": {
"duration": {
"id": "ba5eed78-ccae-4d60-addb-27764d01b15c",
"type": "constant",
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},
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"variableData": null
},
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},
"timestamp": 1706737249433
},
"/models/1/formulas/22ccfc60-7c46-4eb9-b2dc-f77f15e28322/versions/a52a13d2-27b5-46a8-8a7c-5b07370b4720/formula_inputs/afb305c1-b2a5-4d39-9ba8-55015cb39adb": {
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"position": 10,
"resource": {
"id": "383f0d7e-0b18-4ec5-a5a3-6c5915d8bdae",
"name": "Petrolium (demand kWh)",
"image": {
"id": "183cc8f4-167b-4688-84ca-630093adb28a",
"type": "icon",
"icon": {
"iconCodePoint": 58260
},
"web": null
},
"divisible": true
},
"formulaNumber": {
"id": "95dde9a8-6f9e-4353-93d4-bbec50c98837",
"type": "variable",
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"variableData": {
"key": "INPUT_WITH_UPSTREAM::CACHE_AVAILABLE_VALUE::ID383f0d7e-0b18-4ec5-a5a3-6c5915d8bdae"
}
},
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"timestamp": 1706737249433
},
"/models/1/formulas/22ccfc60-7c46-4eb9-b2dc-f77f15e28322/versions/a52a13d2-27b5-46a8-8a7c-5b07370b4720/formula_outputs/57f93866-8477-49ea-a9e0-564b530cc2f8": {
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"resource": {
"id": "0dba77a6-0c3d-47ba-850a-d82dcc8be0c5",
"name": "CO2 from petrol (lbs)",
"image": {
"id": "03867429-93e0-4967-a05d-8e14cc9a58ae",
"type": "icon",
"icon": {
"iconCodePoint": 57444
},
"web": null
},
"divisible": true
},
"formulaNumber": {
"id": "e3dd02e8-b302-4008-a7fa-097c4a0e20f1",
"type": "calculation",
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"calculationData": {
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"variableData": {
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}
},
"rightFormulaNumber": {
"id": "a8c254b5-c9c0-45e6-aab5-c1e444977b60",
"type": "constant",
"constantData": {
"value": "2.44"
},
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},
"mathOperation": "multiply"
},
"ifElseData": null,
"variableData": null
},
"cacheSize": "0",
"timestamp": 1706737249433
},
"/models/1/formulas/22ccfc60-7c46-4eb9-b2dc-f77f15e28322/versions/a52a13d2-27b5-46a8-8a7c-5b07370b4720": {
"versionRef": {
"id": "22ccfc60-7c46-4eb9-b2dc-f77f15e28322",
"ref": "a52a13d2-27b5-46a8-8a7c-5b07370b4720"
},
"visibility": "public",
"name": "Petrolium",
"decoration": {
"image": {
"id": "9780ffa0-ccc0-4da6-92f5-a988ea552f27",
"type": "web",
"icon": null,
"web": {
"url": "https://images.unsplash.com/photo-1648369000096-109763c11e8e?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w0NTA0Mzl8MHwxfHJhbmRvbXx8fHx8fHx8fDE2ODU3MjcwOTF8&ixlib=rb-4.0.3&q=80&w=1080",
"unsplashData": null
}
},
"colorHex": "#ff9e9e9e"
},
"timeSpan": {
"duration": {
"id": "b3a0d00a-5eba-4b16-8e51-973460555c73",
"type": "constant",
"constantData": {
"value": "0"
},
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},
"timeUnit": "days"
},
"timestamp": 1706737249433
}
}
}Generating models in that format was possible! But my success rate was low, and each query was taking almost five minutes and costing tens of cents.
Dumb it down.
When asked in English, GPT-4 was able to describe the components of almost anything you’d want to simulate using good logic. However, I couldn’t effectively get that quality output into our data format, and thus, after some thought, I set out to change the data format.
Cut out the visuals
We were getting all sorts of hallucinated image urls, ugly color hex codes and bad icon codes. Low hanging fruit.
These things were all over the place:
<collapsed json>
"decoration": {
"image": {
"id": "fa7542fb-a67e-4b30-9056-69d47d98ed6e",
"type": "web",
"icon": null,
"web": {
"url": "https://images.unsplash.com/photo-1580137189272-c9379f8864fd?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w0NTA0Mzl8MHwxfHJhbmRvbXx8fHx8fHx8fDE2ODk1MzM1OTV8&ixlib=rb-4.0.3&q=80&w=1080",
"unsplashData": null
}
},
"colorHex": "#ff9e9e9e"
},
</collapsed>My first act was making the various “decoration” fields nullable, and removing them from the instructions.
Don’t tell your Assistant about the rare use cases
Simily lets you define all kinds of number types: constant numbers, random numbers, numbers produced by an equation, numbers defined by the environment etc. However, we are only trying to generate good starter simulations, we don’t need the power features.
I began to hide the existence of all the number types except constant numbers from the instructions.
Blow it up. Dumberer it down. Redefine requirements.
At this point we are mangling our data mode a bit, and while we are seeing improvements, we’re not seeing huge gains. Maybe we need to go back to the drawing board.
Our primary data model has everything necessary to display our Simulations on screen, run queries and updates, serialize efficiently, handle nested simulations, attribute changes to particular users, etc. But we don’t want AI logic for any of those things, we want to use AI for its logic and knowledge in designing simulations.
So here is the secret:
Create a new simplified data model that can be imported and transformed to your app’s sophisticated data model.
Remove all permissions and ownership
You are asking an AI to generate this thing. Assign permissions at import time, at this point we only care about the logic and raw data of the model being generated.
Identify gaps we can fill with traditional algorithms
Id association and generation
Nest content directly (rather than use associations)
De-dupe things in traditional code if necessary.
Positioning
The AIs can generate x,y coordinates, but they are unlikely to be good quality without further instructions and compute power
You can define a consistently pleasant looking layout in code using your own algorithm
Find other concepts that we can eliminate entirely
In Simily, we removed the concept of Connections entirely from our data model
We were able to do this because a connection is defined as a specific resource from one Formula leading to a specific resource of another. Knowing we would have a limited number of Formulas and Resources, we decided we could just connect every input or output, to every other input or output that shares a Resource name.
We also removed the concept of Caches entirely
This concept was just adding another complication to our logic, and rather than have the AI generate these things, we made a general import rule to always add caches in between connected Formulas.
As mentioned earlier, we removed many of the Formula Number Types we didn’t expect to use during generation. The current setup does not support things like time variables.
In your project you might remove concepts like:
Text formats
Could be reduced to a small set of common formats
Formatting could be implied by content context
Attachments
This might be a key tool in user generated content, but you aren’t expecting many from your AI generated content
Avatars
Position fields
Which can easily just be inferred by position in a JSON Array.
Version Ids
Timestamps
Use Query hints
If there is a hard to generate bit of data that is required, consider including a hint you can use to query for the data you need during the import phase.
Images - Simily uses unsplash as an image provider. Rather than have the AI try to generate a valid unsplash url, we ask the AI to generate a good image search term.
Compare and Contrast
Given a simple simulation like the following:
Our simplified data model looks like:
<collapsed json>
{
"name": "A Chicken Coop with only two Formulas",
"image": "chicken_coop",
"formulas": {
"packaging": {
"name": "Egg Packaging",
"image": "egg_carton",
"duration": {
"type": "Constant",
"data": {
"constant": "1"
}
},
"durationUnit": "days",
"inputs": [
{
"resource": "Egg",
"quantity": {
"type": "Constant",
"data": {
"constant": "12"
}
}
}
],
"outputs": [
{
"resource": "Egg Carton",
"quantity": {
"type": "Constant",
"data": {
"constant": "1"
}
}
}
]
},
"chicken": {
"name": "Egg Laying Chicken",
"image": "chicken",
"duration": {
"type": "Constant",
"data": {
"constant": "1"
}
},
"durationUnit": "days",
"inputs": [
{
"resource": "Chicken Feed (lbs)",
"quantity": {
"type": "Constant",
"data": {
"constant": "0.15"
}
}
}
],
"outputs": [
{
"resource": "Egg",
"quantity": {
"type": "Constant",
"data": {
"constant": "1"
}
}
},
{
"resource": "Manure (lbs)",
"quantity": {
"type": "Constant",
"data": {
"constant": "0.1"
}
}
}
]
}
}
}
</collapsed>That same simulation looks like this after import:
<collapsed json>
{
"formatVersion": 2,
"generationTime": "2024-02-01T11:17:56.761",
"name": "A Chicken Coop with only two Formulas",
"targetModelPath": "/models/1/simulations/039fa6bb-0f1b-4ab5-a809-789a41ec670f/versions/HEAD",
"data": {
"/models/1/simulations/039fa6bb-0f1b-4ab5-a809-789a41ec670f/versions/HEAD": {
"versionRef": {
"id": "039fa6bb-0f1b-4ab5-a809-789a41ec670f",
"ref": "HEAD"
},
"visibility": "public",
"name": "A Chicken Coop with only two Formulas",
"rootSystemVersionRef": {
"id": "a8c829c1-944b-4b04-9b82-fba8098d0b28",
"ref": "HEAD"
},
"timestamp": 1706807876762
},
"/models/1/systems/a8c829c1-944b-4b04-9b82-fba8098d0b28/versions/HEAD/entities/7a27d93d-d45b-4eff-b82f-473edbcd722c": {
"id": "7a27d93d-d45b-4eff-b82f-473edbcd722c",
"name": "",
"type": "formula",
"data": {
"id": "78950bae-4ca1-4924-8a84-01cfed79f71d",
"ref": "HEAD"
},
"timestamp": 1706807876762
},
"/models/1/systems/a8c829c1-944b-4b04-9b82-fba8098d0b28/versions/HEAD/entities/ff216b2d-ffad-49cf-9e98-7fcd03ede883": {
"id": "ff216b2d-ffad-49cf-9e98-7fcd03ede883",
"name": "",
"type": "formula",
"data": {
"id": "f57e3a34-26f6-4da4-9a86-a0fd94636693",
"ref": "HEAD"
},
"timestamp": 1706807876763
},
"/models/1/systems/a8c829c1-944b-4b04-9b82-fba8098d0b28/versions/HEAD/entities/83e572af-5641-4397-9fce-c33765827492": {
"id": "83e572af-5641-4397-9fce-c33765827492",
"name": "Egg",
"type": "cache",
"data": {
"id": "1884f9a9-8fc8-4c7c-a6a0-d0e60381361b",
"ref": "HEAD"
},
"timestamp": 1706807876763
},
"/models/1/systems/a8c829c1-944b-4b04-9b82-fba8098d0b28/versions/HEAD/entity_ui_datas/7a27d93d-d45b-4eff-b82f-473edbcd722c": {
"x": 1350,
"y": 720,
"width": 220,
"height": 140,
"timestamp": 1706807876763
},
"/models/1/systems/a8c829c1-944b-4b04-9b82-fba8098d0b28/versions/HEAD/entity_ui_datas/ff216b2d-ffad-49cf-9e98-7fcd03ede883": {
"x": 450,
"y": 480,
"width": 220,
"height": 140,
"timestamp": 1706807876763
},
"/models/1/systems/a8c829c1-944b-4b04-9b82-fba8098d0b28/versions/HEAD/entity_ui_datas/83e572af-5641-4397-9fce-c33765827492": {
"x": 900,
"y": 720,
"width": 140,
"height": 140,
"timestamp": 1706807876763
},
"/models/1/systems/a8c829c1-944b-4b04-9b82-fba8098d0b28/versions/HEAD/connections/06c98f7e-4b80-4c40-8a6d-9dbb4395605a": {
"id": "06c98f7e-4b80-4c40-8a6d-9dbb4395605a",
"sourceEntityId": "83e572af-5641-4397-9fce-c33765827492",
"sourceResourceId": "9b8c18ac-45c1-4a54-9b26-7ab583ad4c20",
"sinkEntityId": "7a27d93d-d45b-4eff-b82f-473edbcd722c",
"sinkResourceId": "5c9b143b-30d7-4683-b5ad-bd027f384dbe",
"pushEnabled": true,
"pushPriority": 1,
"pullEnabled": true,
"pullPriority": 1,
"timestamp": 1706807876763
},
"/models/1/systems/a8c829c1-944b-4b04-9b82-fba8098d0b28/versions/HEAD/connections/d484b6d6-d56d-4482-9f76-267b947474b3": {
"id": "d484b6d6-d56d-4482-9f76-267b947474b3",
"sourceEntityId": "ff216b2d-ffad-49cf-9e98-7fcd03ede883",
"sourceResourceId": "70fd3670-9f29-410c-9e70-c8731b7591f2",
"sinkEntityId": "83e572af-5641-4397-9fce-c33765827492",
"sinkResourceId": "9b8c18ac-45c1-4a54-9b26-7ab583ad4c20",
"pushEnabled": true,
"pushPriority": 1,
"pullEnabled": true,
"pullPriority": 1,
"timestamp": 1706807876763
},
"/models/1/systems/a8c829c1-944b-4b04-9b82-fba8098d0b28/versions/HEAD": {
"versionRef": {
"id": "a8c829c1-944b-4b04-9b82-fba8098d0b28",
"ref": "HEAD"
},
"visibility": "public",
"name": "A Chicken Coop with only two Formulas",
"decoration": {
"image": {
"id": "0555e752-eb88-4572-a71b-12b3286616d5",
"type": "web",
"icon": null,
"web": {
"url": "https://images.unsplash.com/photo-1618346146499-5a503b5e4893?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w0NzcyNDl8MHwxfHJhbmRvbXx8fHx8fHx8fDE3MDY4MDc2NDh8&ixlib=rb-4.0.3&q=80&w=1080",
"unsplashData": {
"id": "J6EpY43_1jg",
"colorHex": "#598c26",
"photoUrls": {
"raw": "https://images.unsplash.com/photo-1618346146499-5a503b5e4893?ixid=M3w0NzcyNDl8MHwxfHJhbmRvbXx8fHx8fHx8fDE3MDY4MDc2NDh8&ixlib=rb-4.0.3",
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"regular": "https://images.unsplash.com/photo-1618346146499-5a503b5e4893?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w0NzcyNDl8MHwxfHJhbmRvbXx8fHx8fHx8fDE3MDY4MDc2NDh8&ixlib=rb-4.0.3&q=80&w=1080",
"small": "https://images.unsplash.com/photo-1618346146499-5a503b5e4893?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w0NzcyNDl8MHwxfHJhbmRvbXx8fHx8fHx8fDE3MDY4MDc2NDh8&ixlib=rb-4.0.3&q=80&w=400",
"thumb": "https://images.unsplash.com/photo-1618346146499-5a503b5e4893?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w0NzcyNDl8MHwxfHJhbmRvbXx8fHx8fHx8fDE3MDY4MDc2NDh8&ixlib=rb-4.0.3&q=80&w=200"
}
}
}
},
"colorHex": "#000000"
},
"timestamp": 1706807876763
},
"/models/1/formulas/78950bae-4ca1-4924-8a84-01cfed79f71d/versions/HEAD/formula_inputs/4484fde0-bded-4a04-a5c8-afd6ed8885a4": {
"id": "4484fde0-bded-4a04-a5c8-afd6ed8885a4",
"position": 0,
"resource": {
"id": "5c9b143b-30d7-4683-b5ad-bd027f384dbe",
"name": "Egg",
"image": {
"id": "2ff64368-d547-4424-a965-2eb8c6b1d77f",
"type": "icon",
"icon": {
"iconCodePoint": 61633
},
"web": null
},
"divisible": true
},
"formulaNumber": {
"id": "26325861-bede-409a-b993-6e67a37f5e2c",
"type": "constant",
"constantData": {
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},
"randomData": null,
"calculationData": null,
"ifElseData": null,
"variableData": null
},
"cacheSize": "0",
"timestamp": 1706807876764
},
"/models/1/formulas/78950bae-4ca1-4924-8a84-01cfed79f71d/versions/HEAD/formula_outputs/b5377319-429b-43cb-b313-e04b67f74133": {
"id": "b5377319-429b-43cb-b313-e04b67f74133",
"position": 0,
"resource": {
"id": "498d0ddc-e4f9-491d-ab46-acd4b239ba10",
"name": "Egg Carton",
"image": {
"id": "895d7028-a09a-448e-ba9c-6bdbc4274eb7",
"type": "icon",
"icon": {
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},
"web": null
},
"divisible": true
},
"formulaNumber": {
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},
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},
"cacheSize": "0",
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},
"/models/1/formulas/78950bae-4ca1-4924-8a84-01cfed79f71d/versions/HEAD": {
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"ref": "HEAD"
},
"visibility": "public",
"name": "Egg Packaging",
"decoration": {
"image": {
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"type": "web",
"icon": null,
"web": {
"url": "https://images.unsplash.com/photo-1585355611444-06154f329e96?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w0NzcyNDl8MHwxfHJhbmRvbXx8fHx8fHx8fDE3MDY4MDc2NDh8&ixlib=rb-4.0.3&q=80&w=1080",
"unsplashData": {
"id": "DNAUkd9Ija0",
"colorHex": "#262626",
"photoUrls": {
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"full": "https://images.unsplash.com/photo-1585355611444-06154f329e96?crop=entropy&cs=srgb&fm=jpg&ixid=M3w0NzcyNDl8MHwxfHJhbmRvbXx8fHx8fHx8fDE3MDY4MDc2NDh8&ixlib=rb-4.0.3&q=85",
"regular": "https://images.unsplash.com/photo-1585355611444-06154f329e96?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w0NzcyNDl8MHwxfHJhbmRvbXx8fHx8fHx8fDE3MDY4MDc2NDh8&ixlib=rb-4.0.3&q=80&w=1080",
"small": "https://images.unsplash.com/photo-1585355611444-06154f329e96?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w0NzcyNDl8MHwxfHJhbmRvbXx8fHx8fHx8fDE3MDY4MDc2NDh8&ixlib=rb-4.0.3&q=80&w=400",
"thumb": "https://images.unsplash.com/photo-1585355611444-06154f329e96?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w0NzcyNDl8MHwxfHJhbmRvbXx8fHx8fHx8fDE3MDY4MDc2NDh8&ixlib=rb-4.0.3&q=80&w=200"
}
}
}
},
"colorHex": "#000000"
},
"timeSpan": {
"duration": {
"id": "ef87c076-1ec1-420b-8b1d-2ef89317d03a",
"type": "constant",
"constantData": {
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},
"randomData": null,
"calculationData": null,
"ifElseData": null,
"variableData": null
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</collapsed>Conclusion
The simplified version of our model is simpler! This results in fewer decisions to be made by the AI and reduces the amount of raw data we need to generate and transfer. Indeed the outputs generated using our simplified model are two or three times faster to produce, rarely contain invalid data, and do a much better job of representing the simple Simulations we are hoping to create. Big wins!