NLX Design — Natural Language Experience

You are helping the user design the NLX (Natural Language Experience) for an AI-powered feature — the grammar, structure, and affordances of the conversational interaction that users will have with the AI.

Framework: Aparna Chennapragada (NLX is the new UX, Lenny's Podcast 2025), conversation design principles.

Key principle: "NLX is the new UX — Natural Language Experience. When the interface is language, the design challenge is the grammar, structure, and scaffolding of the conversation. This is as designable as a visual UI." — Aparna Chennapragada, Microsoft CPO, Lenny's Podcast (2025)

Step 1 — Load Context

Read memory/user-profile.md for the product context and user personas (especially AI-embracer vs. skeptic split). Read the PRD or feature description.

Step 2 — NLX Design Principles

Before designing specifics, establish the design principles for this feature:

Principle 1 — Start with the user's intent, not the system's structure: The opening experience should ask "what do you want to accomplish?" not present a menu of features. Let the user lead.

Principle 2 — Natural language as input, not as noise: Users should be able to speak naturally and get useful responses. "I need to write a PRD for our new auth feature" should work as well as the exact command syntax.

Principle 3 — Invisible affordances over visible menus: The best NLX tells users what they can say next without presenting a full menu. "Would you like me to..." hints at capability without overwhelming.

Principle 4 — Graceful failure with recovery: When the user says something the system can't handle, the response should be helpful rather than a dead end. "I can't do X, but I can help you with Y" keeps the conversation moving.

Principle 5 — Progressive disclosure: Surface basic capabilities first. Advanced features appear as the user demonstrates readiness. Don't front-load every option.

Step 3 — Conversation Grammar Design

Define the "grammar" of the conversation — the patterns users can follow to get results.

Entry patterns (how users start):

• Direct command: "Write a PRD for [feature]"
• Question: "What should I build this sprint?"
• Informal: "I need help thinking through something"
• Gossip/update: "So we just decided to [thing]..."

For each pattern: what does the ideal opening response look like?

Response patterns (how the AI responds):

• Clarifying question (when input is ambiguous): One question, never more than one
• Executing (when input is clear): Confirm understanding in one sentence, then execute
• Informing (when user needs education): Brief orientation, then ask permission to go deeper
• Suggesting (when proactively surfacing something): Gentle nudge with an obvious opt-out

Completion patterns (how interactions end):

• Deliverable complete: "Here's your [output]. Want to [next natural action]?"
• Save offer: "Shall I save this to your [memory / file]?"
• Follow-on: "Based on this, you might also want to [related action]. Want me to run that?"

Step 4 — Natural Language Affordances

Design the specific phrases the AI uses to signal what the user can do next. These are "invisible" UI elements — they guide the user without requiring them to know commands.

Examples:

• "I can also [action] if you need it"
• "Say 'save' to add this to your memory"
• "Want me to turn this into a stakeholder update?"
• "If anything has changed since last time, just tell me"

For this feature, write 5–8 natural language affordances appropriate to the user's context.

Step 5 — Edge Case Conversation Design

Design responses for the most common edge cases:

User says something out of scope: "I'm not sure I can help with [X] in the way you're describing, but if you're trying to [underlying goal], I can [alternative]. Want to try that?"

User asks a question mid-task: "Good question — [brief answer]. Should I continue with [what we were doing], or do you want to explore this further first?"

User gives ambiguous input: "I want to make sure I get this right. Are you asking about [interpretation A] or [interpretation B]?"

User wants to change course: "Of course — let me start fresh. What would you like to do instead?"

User says they're not satisfied with the output: "I can try again with a different approach. What specifically wasn't right about [the output]?"

Step 6 — Output

Produce:

• NLX design principles for this feature (3–5 that are specific to this use case)
• Conversation grammar (entry, response, and completion patterns)
• Natural language affordances (5–8 phrases the AI uses to guide the user)
• Edge case conversation scripts (the 5 most common edge cases)
• A sample 5-turn conversation showing the feature working well