A guide to designing meaningful AI features that scale

As a product leader, you're likely exploring how to incorporate Large Language Models (LLMs) into your offerings. Whether driven by competitive pressure, rising user expectations, or untapped efficiencies, LLM integration has moved beyond experimentation into serious product strategy.

This guide helps you move past the hype and make informed, scalable decisions.

Part 1: What LLMs Are Good At—and Where They Struggle

Before building, it’s important to understand where LLMs perform well:

Strengths:

• Text generation: Summarising, expanding, rewriting, or explaining content

• Data extraction: Pulling structured information from documents or images

• Classification: Tagging, flagging, or categorising content

• Dialogue interaction: Providing helpful, context-sensitive responses

• Code assistance: Offering templates, fixes, and documentation

Limitations:

• Factual accuracy: Can hallucinate or misstate information

• Current events: Lacks live data unless explicitly provided

• Narrow domains: Needs tuning or context for specialist tasks

Knowing these trade-offs helps you identify practical opportunities—not just novel features.

Part 2: Scoping the Right Use Cases

Start from the user, not the model.

Ask:

• Where do users repeat the same tasks?

• Which flows require interpretation of unstructured data?

• What decisions are slowed by lack of summarised input?

• Where is there content or decision bottlenecks caused by manual triage, tagging, or review?

The best uses of LLMs align with existing friction, not innovation theatre.

Part 3: Designing with Constraints That Scale

Instead of broad goals like:

• “Add an AI chatbot”

• “Use LLMs in onboarding”

Focus on contained experiences where input, processing, and outcome can be clearly defined. For example:

These scoped implementations make it easier to monitor quality, improve iteratively, and explain results to stakeholders.

Part 4: Prototyping AI Features Without Heavy Lifts

Before operationalising any LLM integration, it’s crucial to prototype in a way that reveals value early—without requiring infrastructure changes or dedicated engineering teams.

Here’s a practical approach:

1. Define the Smallest Viable Use Case

Identify a narrow problem that touches real users or teams. Look for high-effort, low-complexity tasks:

• Tagging inbound support messages

• Extracting fields from feedback forms

• Reviewing documents during onboarding

• Verifying product images for retail

Keep it real, not theoretical.

2. Write the Ideal Prompt

Think of the AI interaction like designing an API:

• What should the model know?

• What’s the exact input and expected output?

• What would a “good” result look like?

• How should the response be structured or labelled?

This becomes your working spec.

3. Test With Real Inputs

Use real examples to explore:

• Are outputs consistent?

• Is tone and content aligned with your goals?

• Do results require post-editing or human review?

No integration required at this stage—just prompt iteration and validation.

4. Package Learnings for Repeatability

Capture each working prototype as:

• A clear input schema

• A refined instruction

• A structured output format

• An understanding of performance expectations

This forms a unit that can be shared, reused, and extended—without starting from scratch each time.

Part 5: Build Iteratively, Scale Intelligently

Once you’ve validated value, build incrementally:

1. Pilot - Deploy a lightweight version into a real workflow. Focus on delivering one meaningful outcome.

2. Refine - Use real-world feedback to improve accuracy, structure, and user experience. This is where prompt iteration and tuning have the most impact.

3. Expand - Extend proven patterns to similar use cases. If you’ve built a reliable summarisation feature, apply the same logic to onboarding, help docs, or internal knowledge bases.

4. Govern - Version your AI components, monitor cost and performance, and track feedback. AI is now part of your product infrastructure—treat it accordingly.

Part 6: Designing the Full User Experience

Strong technical performance won’t matter if the user experience is brittle or confusing.

Design considerations:

• Clarity: Do users know when they’re seeing AI content?

• Control: Can they modify or reject suggestions?

• Confidence: Is the system’s certainty clearly conveyed?

• Fallbacks: What happens when the output isn’t useful?

• Feedback: Can users report issues or improve results over time?

Well-designed interfaces improve trust, usage, and overall satisfaction.

Part 7: Cross-Functional Collaboration

With Engineering

• Treat LLM features as services with versioning and monitoring

• Plan for error handling, rate limits, and response time variability

With Design

• Show AI confidence clearly

• Let users interact with outputs in flexible ways

With Content & Legal

• Set rules for acceptable output

• Review content risks and audit mechanisms

• Document how AI suggestions should be presented to users

Part 8: Common Pitfalls to Avoid

• Overpromising intelligence: Be clear about what AI can and can’t do

• Skipping the feedback loop: Make iteration part of the process

• Forgetting the human handoff: Let people override or step in

• Treating AI as a single project: Think of it as a system of evolving capabilities

Final Thought

The most successful LLM integrations feel like natural product enhancements—not centrepieces. When well-scoped, they reduce friction, accelerate workflows, and free users to focus on what matters most.

If you'd like to start building your first AI prototypes or scale existing proof-of-concepts into production, then check out app.rightbrain.ai.