AI-Generated Recipe App

AI-Generated
Recipe App

Recipe Gen AI is an AI-powered feature in the SideChef app that helps users generate complete, cookable recipes based on ingredients they have, dietary preferences, and cooking constraints.

Client:

SideChef App

Role:

Lead Product Designer (UX + UI + Storytelling)

Scope:

End-to-end design (0 → 1), workflow, UI system, Motion Design

Timeline:

6–8 weeks

Team:

PM + Design (me) + Engineers

Background & Why This Matters

From Image Search to AI Recipe Generation

When people want to recreate a dish they see, they often start by searching the image on Google Image Search. However, image search usually returns visually similar dishes rather than an exact, reliable recipe.

As a result, many users turn to AI tools like GPT to generate recipes instead. While AI speeds up generation, most outputs remain text-only and lack execution support for real cooking, creating a clear opportunity for SideChef to bridge inspiration and action.

Research & Insights

Interviewed 35 recipe content creators and food brand marketers

We conducted quantitative research with 412 SideChef users, combining survey data and behavioral analysis of recipe search and save events across dining-out and social media scenarios.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Problem Statement

Food brands and creators need to generate large volumes of high-quality food visuals, but existing workflows are expensive and slow, and generic AI tools create inconsistent results that require heavy prompting and rework.

Design Goals × Metrics

How We Measured
Success

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Make AI usable for non-experts

Metric: Prompt edits per generation ↓ 40% Signal: Users rely on UI presets vs free-typing prompts

Make AI usable for non-experts

Metric: Prompt edits per generation ↓ 40% Signal: Users rely on UI presets vs free-typing prompts

Make AI usable for non-experts

Metric: Prompt edits per generation ↓ 40% Signal: Users rely on UI presets vs free-typing prompts

Make AI usable for non-experts

Metric: Prompt edits per generation ↓ 40% Signal: Users rely on UI presets vs free-typing prompts

Enable scalable production

Metric: Multi-asset creation per session ↑Signal:Users generate 5+ images/session for a campaign pack

Enable scalable production

Metric: Multi-asset creation per session ↑Signal:Users generate 5+ images/session for a campaign pack

Enable scalable production

Metric: Multi-asset creation per session ↑Signal:Users generate 5+ images/session for a campaign pack

Enable scalable production

Metric: Multi-asset creation per session ↑Signal:Users generate 5+ images/session for a campaign pack

Solution (Workflow-First AI Studio)

Replace prompt-heavy input with structured visual controls

Instead of relying on free-text prompts, we exposed key food photography variables as explicit UI controls,

These variables are what actually drive visual consistency. Making them explicit reduces ambiguity and lowers the learning curve for non-expert users.

Enable user-created presets through reusable settings

Rather than providing system-defined presets, the beta allows users to turn a successful configuration into a reusable baseline via “Use Settings”.

Brand teams care about repeating their definition of “good”. User-created presets preserve consistency without enforcing assumptions too early.

Threaded history with one-click reuse & regenerate

The generation history is designed not only for review, but for fast reuse and regeneration.

In creative workflows, restarting is expensive.
By allowing users to reuse and regenerate directly from history, we turn past success into the fastest path forward.

Mode-based workflow switching

Users can switch modes instantly without leaving the page or losing context.

In real production workflows, teams move fluidly between generating, refining, and branding assets. Mode-based switching reduces friction and prevents users from restarting tasks in separate tools.

Integrated Product Placement for brand-ready visuals

Users can insert branded products into generated visuals without leaving the generation context, instead of exporting images to external editing tools.

Brand and commerce teams often need visuals that include specific products.
Embedding product placement into the same workflow reduces handoff friction and shortens the path from generation to publishable assets.

Before / After comparison (Comingsoon)

To help users evaluate AI outputs more efficiently, we introduced before / after comparison between the reference image and generated results.

ComingSoon

In creative workflows, decision-making is often the slowest step. Side-by-side comparison reduces cognitive load and shortens iteration cycles by making differences immediately visible.

Smart Style Guideline Generation from Uploaded References

To help brand teams scale visual consistency more efficiently, we explored a solution that allows users to upload existing photo guidelines (images or PDFs), which the system can then translate into structured, usable style guidelines.

ComingSoon

Many brands already have established visual standards. Translating existing guidelines into machine-readable controls significantly reduces setup cost and accelerates adoption for enterprise users.

Results & Impact

Deeper User Engagement

These signals suggest that guided controls, reusable settings, and integrated workflows effectively reduce iteration cost and support real-world content production.

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Avg. Total Usage Time / User

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Avg. Total Usage Time / User

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Avg. Total Usage Time / User

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Avg. Images Generated / User

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Avg. Images Generated / User

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Avg. Images Generated / User

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NPS Score

(Users rate the product as easy and valuable to use.)

NPS Score

(Users rate the product as easy and valuable to use.)

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MAU Growth

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MAU Growth

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MAU Growth

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Avg. Sessions / User

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Avg. Sessions / User

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Avg. Sessions / User

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AI-Generated Recipe App

AI-GeneratedRecipe App

Recipe Gen AI is an AI-powered feature in the SideChef app that helps users generate complete, cookable recipes based on ingredients they have, dietary preferences, and cooking constraints.

Client:

SideChef App

Role:

Lead Product Designer (UX + UI + Storytelling)

Scope:

End-to-end design (0 → 1), workflow, UI system, Motion Design

Timeline:

6–8 weeks

Team:

PM + Design (me) + Engineers

Background & Why This Matters

From Image Search to AI Recipe Generation

When people want to recreate a dish they see, they often start by searching the image on Google Image Search. However, image search usually returns visually similar dishes rather than an exact, reliable recipe.

As a result, many users turn to AI tools like GPT to generate recipes instead. While AI speeds up generation, most outputs remain text-only and lack execution support for real cooking, creating a clear opportunity for SideChef to bridge inspiration and action.

Research & Insights

Research & Insights

Research & Insights

Interviewed 35 recipe content creators and food brand marketers

We conducted quantitative research with 412 SideChef users, combining survey data and behavioral analysis of recipe search and save events across dining-out and social media scenarios.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Iteration cost is the real bottleneck

Users spend more time rewriting prompts and regenerating than producing usable assets.

Brand consistency is harder than visual quality

Even when images look “good”, they often don’t match the brand’s identity across a set.

Most users want controls, not prompts

Users prefer selecting options like angle / plating / lighting / background / style rather than learning prompt engineering.

Workflow beats features

What users need is a repeatable production pipeline, not “one generate button”.

Problem Statement

Problem Statement

Problem Statement

Design Goals × Metrics

Design Goals × Metrics

Design Goals × Metrics

How We MeasuredSuccess

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Reduce production time

Metric: Time-to-first-usable image ↓ 60% Signal: Users export/save within the first 3 generations

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Improve brand consistency

Metric: Consistency score ↑ from 2.6 → 4.0 / 5 Signal: Fewer “style reset” regenerations per asset set

Make AI usable for non-experts

AI-Generated
Recipe App

How We Measured
Success

In creative workflows, restarting is expensive.
By allowing users to reuse and regenerate directly from history, we turn past success into the fastest path forward.

Brand and commerce teams often need visuals that include specific products.
Embedding product placement into the same workflow reduces handoff friction and shortens the path from generation to publishable assets.