1. The Demo Trap and How to Avoid It
Every AI startup has a beautiful demo. GPT-4 makes anyone look like a genius in a controlled environment. But demos don't survive contact with real users.
The demo trap happens when teams spend months perfecting a showcase that falls apart under real-world conditions:
- Latency that's acceptable in demos becomes unbearable at scale
- Edge cases that never appeared in testing surface immediately
- Costs that seemed manageable multiply unexpectedly
- Users interact with AI features in ways you never anticipated
The solution: ship to production as fast as possible with a subset of users. Real usage data is worth more than months of internal testing.
2. The 30-Day AI MVP Framework
Here's the framework we use to ship AI features in 30 days:
Week 1: Scope and API
- • Define ONE user workflow to augment
- • Build the API wrapper around your LLM provider
- • Set up basic logging and cost tracking
Week 2: Integration and UI
- • Connect AI to your existing product
- • Build minimal UI (streaming responses, loading states)
- • Implement basic error handling
Week 3: Eval and Iteration
- • Deploy to 5-10 beta users
- • Collect feedback and failure cases
- • Iterate on prompts based on real data
Week 4: Production
- • Add rate limiting and fallbacks
- • Implement monitoring and alerts
- • Roll out to broader user base
3. Technical Decisions That Speed You Up
Speed comes from making good default decisions:
- Start with GPT-4 or Claude: Don't optimize for cost until you have product-market fit. The best model gives you signal on whether the idea works.
- Use structured outputs: Function calling or JSON mode eliminates parsing headaches and makes your AI predictable.
- Stream responses: Perceived latency matters more than actual latency. Streaming makes 3-second responses feel instant.
- Cache aggressively: Identical prompts should return cached results. This cuts costs and improves latency.
- Delay fine-tuning: Prompt engineering gets you 80% of the way. Fine-tune only when you have proof that prompts can't solve the problem.
4. Building Evaluation Loops Early
You can't improve what you can't measure. From day one, you need:
- Logging: Every prompt, response, and latency metric
- User feedback: Simple thumbs up/down on AI outputs
- Golden dataset: 50-100 examples of ideal input/output pairs
- Automated eval: Run your golden dataset on every prompt change
This infrastructure pays for itself within weeks. Every prompt change becomes testable against known baselines.
5. Production Hardening Patterns
Production AI requires specific patterns:
- Graceful degradation: When AI fails, fall back to non-AI behavior. Never let AI failures break core product functionality.
- Rate limiting: Protect against both costs and abuse. Implement per-user and global limits.
- Timeout handling: AI APIs are slow and unreliable. Build retry logic with exponential backoff.
- Content filtering: Both input and output need safety checks. Don't rely solely on the model's built-in filters.
6. Team Structure for AI Velocity
The fastest AI teams are small and cross-functional:
- 1 Product person: Owns user problems and prioritization
- 1-2 Full-stack engineers: Build the integration and UI
- 1 AI engineer: Owns prompts, evals, and model selection
This is exactly the structure we use at HyperNest when embedding with AI startups. The key is keeping the team small enough to iterate daily.