Since late 2023, I've built GenAI applications for three different industries: mindfulness, marketing, and manufacturing. Each project taught me something new about what actually matters when you deploy these systems.
Here's what worked, what failed, and what I suggest that you should focus on first.
The Three Projects That Taught Me Everything
Project 1: Mindfulness Chatbot (10M+ users)
I built a serverless chatbot using Amazon Bedrock with MongoDB vector store. Used Lambda, S3, and DynamoDB for the architecture. The bot answered questions about courses and gave personalized recommendations to users. This was my first real GenAI project, and I learned a lot about handling large user bases.
Project 2: Speech-to-Speech Marketing System (70% cost reduction)
I developed a complete system that automated first-contact validation, qualifying questions, and callback scheduling. The system handled voice conversations and reduced costs by up to 70%. This taught me about real-time processing and reliability.
Project 3: Manufacturing Support Agent (80K+ tickets/month)
I created an agent that learned from recorded call transcriptions to answer support questions for a manufacturing company's call center. This project showed me how to work with existing data and integrate with legacy systems.
The Six Risk Areas That Actually Matter
1. Model Performance vs. Cost Trade-offs
The lesson: Don't try to save money on cheap models that don't work well.
We spent weeks trying to make Claude Haiku work for complex support queries. The responses were fast but often missed important context or gave incomplete answers. The model was cheap, but customers were not happy with the results.
When we switched to a more powerful model, response quality improved dramatically. Yes, it cost more, but the results were much better.
What to monitor:
- Streaming performance (if your model supports it)
- Response latency (how fast responses come back)
- Cost per interaction
- Answer accuracy and completeness
Cost reality: Sometimes paying 3x more per request saves you from rebuilding the entire system. A cheap model that doesn't work is actually more expensive than a good model that works.
Practical tip: Start with a good model, then optimize costs later. Don't start with the cheapest option.
2. Language and Knowledge Base Alignment
The problem: Our manufacturing client had an English knowledge base but needed to answer customer questions in Spanish, Portuguese, and German.
What happened: The model struggled to translate technical context accurately. Technical terms got lost in translation. Customer satisfaction dropped because answers were not precise.
The fix: Build your knowledge base in the languages you need to support, or use dedicated translation services before querying your knowledge base.
Key insight: Large language models can translate text, but they can't create knowledge that isn't there. If your knowledge base doesn't have the right information in the right language, the model will struggle.
What we learned: Translation quality depends on the complexity of your domain. Simple questions work fine, but technical support requires knowledge base content in the target language.
3. Hallucination Detection and Guardrails
Reality check: Amazon Bedrock Guardrails catch obvious problems, but subtle hallucinations slip through.
Hallucination happens when the model creates information that seems real but is actually false. This is one of the biggest risks in production systems.
What works:
- Use Amazon Bedrock Evaluations for systematic testing
- Implement fact-checking against your knowledge base
- Set up human review for high-stakes responses
- Create evaluation datasets from real customer interactions, not fake data
Real example from the mindfulness app: We discovered the bot was recommending meditation courses that didn't exist. Users were clicking on these recommendations and getting error pages. We fixed this by validating all course references against our actual course catalog before sending responses.
How to detect hallucinations:
- Check facts against your knowledge base
- Look for inconsistent information in responses
- Monitor user feedback for confusion
- Set up automated checks for common hallucination patterns
4. Data Security and Privacy
The basics that matter:
- Encrypt customer data at rest and in transit
- Remove or redact PII (Personal Identifiable Information) and PHI (Protected Health Information) before processing
- Use VPC endpoints for all Bedrock calls
- Implement customer-managed KMS keys for sensitive data
The speech-to-speech project: We handled sensitive data during calls, so we leveraged automatic PII detection and redaction before any data reached the model. This included removing social security numbers, credit card numbers, and addresses from transcripts.
Security layers we implemented:
- Network security: VPC endpoints and private subnets
- Data encryption: At rest and in transit
- Access control: IAM roles with minimal permissions
- Data sanitization: Automatic PII/PHI removal
- Audit logging: Complete request/response logging
Important: Plan your security architecture before you start coding. Adding security later is much harder.
5. Reliability and Fallback Strategies
Models fail. APIs hit limits. Your system needs to handle this gracefully.
In production, things will go wrong. Models will be unavailable, APIs will hit rate limits, and costs might spike unexpectedly. Your application needs to work even when these things happen.
What we implemented:
- Retry mechanisms for failed requests (with exponential backoff)
- Cost alarms to prevent surprise bills
- Fallback to human agents for complex queries
- Quota monitoring for service limits
- Multiple model options for different scenarios
Real example: During a traffic spike, our primary model hit rate limits. Our fallback system automatically switched to a secondary model, keeping the service running. Users experienced slightly slower responses but didn't see errors.
Fallback strategies that work:
- Use multiple models (primary and backup)
- Implement graceful degradation (reduced functionality instead of complete failure)
- Cache common responses to reduce API calls
- Set up automatic alerts for service issues
6. Framework Dependencies and Management
The lesson: Frameworks like LangChain can speed up development but create new risks.
We used LangChain in our first project because it looked easy to implement. It did boost our development speed a lot. But over one year, we faced several problems:
What went wrong:
- Many APIs became deprecated quickly
- We had to update our code constantly
- Dependencies management was very difficult
- Less flexibility to customize specific behaviors
The trade-off:
- Pros: Faster development, ready-made components, good documentation
- Cons: Less control, frequent updates needed, dependency management issues
What we learned:
- Frameworks are good for prototypes and simple applications
- For complex systems, consider building some parts from scratch
- Always check the framework's update frequency and stability
- Have a plan for when dependencies break
Practical tip: Start with frameworks for speed, but be ready to replace parts with custom code when you need more control.
The Implementation Strategy That Actually Works
Start Simple
Don't make your first deployment complicated. Focus on these core elements:
- Basic prompt engineering (clear, specific prompts)
- Simple RAG implementation (retrieval-augmented generation)
- Cost monitoring and alerts
- Basic security (VPC endpoints, encryption)
Why simple works: Complex systems have more failure points. Start with something that works, then add features.
Test Extensively
Spend serious time on testing. We learned this the hard way across all three projects.
Testing approach:
- Test with real customer data, not synthetic examples
- Create evaluation datasets from actual use cases
- Set up automated testing pipelines
- Include edge cases and error scenarios
What to test:
- Response accuracy and completeness
- Handling of unexpected inputs
- Performance under load
- Cost scaling with usage
- Security and privacy compliance
Time investment: Plan to spend 30-40% of your development time on testing. This seems like a lot, but it prevents major issues in production.
Accept Non-Determinism
Large language models will never give you the exact same response twice. You can adjust temperature and other parameters, but responses will always vary slightly.
Plan for this reality:
- Build your evaluation around ranges of acceptable responses, not exact matches
- Focus on response quality and accuracy, not exact wording
- Create multiple acceptable answer examples for testing
- Train your team to expect variation in responses
What We Got Wrong (And You Probably Will Too)
1. Underestimating prompt engineering time
We thought we could create good prompts quickly. Wrong. Good prompts take weeks of iteration and testing.
Time investment: Plan for 2-3 weeks of prompt engineering for complex applications. Simple chatbots might take 1 week, but specialized applications take longer.
2. Ignoring retry mechanisms
Our first deployments had no retry logic. When requests failed, they just failed. Users saw error messages instead of helpful responses.
The fix: Add retry mechanisms from day one. Use exponential backoff to avoid overwhelming services.
3. Choosing models based on cost alone
Haiku was cheap but couldn't handle our use cases well. We wasted weeks trying to make it work instead of using a better model.
Lesson: Sometimes you need to pay more for better results. Calculate the total cost of ownership, not just the per-request cost.
4. Not setting up cost alarms early
Our first month's bill was a surprise. We didn't realize how quickly costs could add up with high usage.
Prevention: Set up cost monitoring and alerts before you deploy. Start with conservative limits and adjust based on actual usage.
5. Assuming one model fits all use cases
We tried to use the same model for everything. Some tasks needed reasoning, others needed speed, and some needed multilingual support.
Better approach: Match models to specific use cases. Use faster models for simple tasks and more powerful models for complex reasoning.
The Bottom Line
GenAI risk management isn't about implementing every possible safeguard. It's about understanding your specific risks and building systems that work reliably for your users.
Start with the basics: security, cost monitoring, and thorough testing. Add complexity as you learn what actually breaks in production.
Most importantly, plan for failure. Your GenAI system will have issues. The question is whether you'll catch them before your customers do.
Key takeaway: Spend more time on prompt engineering and testing than you think you need. It's much cheaper to fix problems during development than after deployment.
Final advice: Don't try to build the perfect system on your first try. Build something that works, deploy it carefully, and improve it based on real user feedback.
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