Ai prompt ux

AI Prompt UX

Design the input side of AI interactions - how users express their intent, refine their requests, and learn to communicate effectively with AI. The CRAFT framework treats the prompt experience as a design surface, not just a text box.

Core Principle

A blank text box is not a prompt UX. It is an abdication of design. The empty chat input puts 100% of the burden on the user to figure out what to say, how to say it, and what's possible. Great prompt UX reduces that burden to near zero while preserving unlimited expressiveness for power users.

---

The CRAFT Framework

Letter	Principle	Design Question
C	Constrain Productively	Does the input interface guide users toward better prompts without limiting expressiveness?
R	Reveal Capabilities	Can users discover what's possible through the input experience itself?
A	Assist Construction	Does the interface help users build complex prompts piece by piece?
F	Format Intelligently	Does the system interpret intent regardless of how the user formatted their input?
T	Teach Through Interaction	Does each interaction make the user slightly better at prompting?

---

The Prompt Input Spectrum

Not every AI product should use a blank text box. Choose the right input pattern for your context:

Pattern	User Freedom	Guidance Level	Best For	Example
Free text	Maximum	Minimum	Expert users, open-ended exploration	ChatGPT main input
Guided text	High	Medium	Most users, most products	Text box with placeholder text: "Ask me to analyze, write, or research..."
Template selection	Medium	High	Specific workflows, recurring tasks	"Summarize this document" / "Compare these options" / "Draft an email about..."
Structured form	Low	Maximum	Domain-specific tools, non-technical users	Form fields: Subject, Tone, Length, Audience, Key points
Hybrid	Variable	Variable	Products serving diverse users	Text box + expandable "Advanced options" panel

Selection Criteria

If your users are...	And the task is...	Use this pattern
Technical, experienced with AI	Open-ended, creative	Free text or guided text
Non-technical, new to AI	Specific, repeatable	Template selection or structured form
Mixed expertise	Variable complexity	Hybrid (text + templates + structured options)
Under time pressure	High-frequency, quick	Template selection with one-click execution

---

Empty State Design

The moment before the first prompt is the most critical UX moment.

The Empty State Hierarchy

Priority	Element	Purpose	Example
1	Single clear action	Tell the user exactly what to do first	Input field with cursor blinking, focused
2	Capability hints	Show 3-4 things the AI can do	Clickable suggestion chips: "Summarize a doc" / "Write an email" / "Analyze data"
3	Example prompts	Show what good prompts look like	"Try: 'Compare the quarterly revenue trends and highlight anomalies'"
4	Recent history	Bring users back to where they left off	"Continue your conversation about Q2 planning"

Anti-pattern: An empty screen with just a text box and "How can I help you?" This communicates nothing about capabilities, nothing about prompt quality, and nothing about what to expect.

---

Prompt Suggestion Systems

Suggestion Types

Type	When It Appears	What It Contains	Example
Pre-prompt suggestions	Before user types anything	Common starting points for this context	Chips: "Summarize" / "Translate" / "Explain"
Inline autocomplete	While user is typing	Completion of the current prompt	User types "Help me write" → suggestion: "...a professional email to decline a meeting"
Follow-up suggestions	After AI responds	Logical next questions or actions	"Go deeper on point 2" / "Make it shorter" / "Generate alternatives"
Refinement suggestions	When output quality is low	Ways to improve the prompt	"Try being more specific about the time period"

Suggestion Quality Rules

Rule	Why It Matters
Suggestions must be contextual, not generic	"Tell me more" is not useful. "Explain the pricing implications" is
Limit to 3-5 suggestions at a time	More than 5 creates decision paralysis
Suggestions should be full, executable prompts	One click should produce a result, not just populate the input field
Rotate suggestions to prevent stale UX	Same 3 suggestions every time = users ignore them after day 2
Power users should be able to hide suggestions	Don't force training wheels on experts

---

Multi-Modal Prompt Design

Modern AI accepts more than text. Design for all input modes:

Input Mode	Design Considerations	UX Pattern
Text	Character limit, formatting support, code blocks	Standard text area with markdown support
File upload	Supported formats, size limits, processing time	Drag-and-drop zone with format badges
Image/screenshot	Resolution requirements, what the AI "sees"	Paste or drop with annotation overlay: "What should I focus on in this image?"
Voice	Transcription accuracy, ambient noise, language	Push-to-talk with real-time transcription preview
URL/link	What gets fetched, what's processed, privacy implications	Paste field with preview: "I'll read [title] - what would you like to know about it?"
Structured data	CSV, JSON, database connections	Upload + preview table + "What would you like to analyze?"

Multi-Modal Orchestration

When users can input multiple modes simultaneously:

Combination	Design Pattern	Example
Text + Image	Text as instruction, image as context	"What's wrong with this UI?" + [screenshot]
Text + File	Text as question, file as data source	"Summarize the key findings" + [PDF upload]
Text + URL	Text as analysis angle, URL as source	"Compare this pricing page to our competitors" + [URL]

Rule: When multiple inputs are provided, the AI should acknowledge ALL inputs in its response. Don't silently ignore the attachment.

---

Prompt History & Reuse

Prompt Library Pattern

For products where users repeatedly need similar AI assistance:

Feature	Implementation	Benefit
Prompt history	Searchable list of past prompts with their results	Quick reuse of successful prompts
Saved prompts	User-created templates with variables: "Write a {tone} email to {recipient} about {topic}"	Repeatability without retyping
Shared prompts	Team-visible prompt library	Organizational knowledge sharing
Prompt analytics	Show which prompts produce the best results	Data-driven prompt improvement

---

The Prompt Refinement Loop

Most good AI interactions require 2-3 iterations. Design for the refinement cycle, not just the first prompt.

Refinement Patterns

Pattern	How It Works	When to Use
Inline edit	User modifies the original prompt and re-runs	When the core intent was right but details need adjustment
Follow-up instruction	"Make it shorter" / "Focus more on the technical details"	When the direction is right but output needs tuning
Branching	"Try a completely different approach"	When the first attempt was fundamentally wrong
Parameter adjustment	Sliders or dropdowns for tone, length, formality, creativity	When the AI supports tunable parameters
Undo and rephrase	Discard current response, try a new prompt	When the user wants a fresh start without losing context

---

Anti-Patterns

Pattern	Why It Fails
Blank text box with no guidance	Users don't know what's possible, write bad prompts, get bad results, leave
"Be more specific" error messages	Tells the user they're wrong but doesn't help them be right
Character limits without explanation	"Prompt too long" - how long? What to cut? Why is there a limit?
Hiding prompt history	Users can't learn from their own past successes
Suggestion chips that don't do anything	Chips that only populate the text box instead of executing the prompt
Auto-submitting on paste	Users paste text as context, not as a prompt. Don't fire automatically

---

Quick Reference

Task	Framework Element	Key Deliverable
Design input for new AI product	Prompt Input Spectrum + Empty State	Input pattern selection + empty state design
Reduce "I don't know what to type"	Suggestion Systems	Pre-prompt, inline, and follow-up suggestion architecture
Add multi-modal input	Multi-Modal Prompt Design table	Input mode support matrix + orchestration rules
Improve prompt quality across user base	Refinement Loop patterns	Iteration flow design + refinement shortcuts
Build enterprise prompt management	Prompt Library Pattern	Shared prompt library with history and analytics

Integration

Works with: ai-onboarding-calibration (teaching prompting during onboarding), ai-conversation-architect (prompt as first turn in dialogue), ai-feedback-loops (feedback on prompt suggestions), ai-personalization-ethics (personalized prompt suggestions).