Introduction
Generative AI is reshaping how businesses operate—automating customer service, generating marketing content, powering intelligent assistants, and much more. But the way you build GenAI products is very different from how you’d build traditional software.
At Valstrand, we help organizations navigate these differences with strategic project management and tailored AI workflow automation. In this post, we’ll explore how the GenAI project lifecycle unfolds—and why it demands a unique mindset, skillset, and methodology.
🔍 Key Differences at a Glance
| Category | Traditional Projects | Generative AI Projects |
|---|---|---|
| Requirements | Defined upfront | Evolve through experimentation |
| Deliverables | Code modules, features | Prompts, models, fine-tuned outputs |
| Evaluation | Unit testing, QA | Prompt output quality, hallucination rate |
| Tech Stack | APIs, UI, DB | Pre-trained models, prompts, feedback loops |
| Stakeholders | Dev, QA, Biz | + Data scientists, ML engineers, Prompt engineers |
| Success Metrics | Uptime, latency, bugs | Factuality, bias, safety, user trust |
The GenAI Project Lifecycle
1. Use Case Discovery
Identify problems that are creative, repetitive, or language-heavy.
Example:
- A bank uses GenAI to summarize complex investment reports for retail clients.
- An HR tech firm builds a resume review chatbot trained on recruiter feedback.
At this stage, PMs assess feasibility, ROI, ethical impact, and compliance risk.
2. Data & Model Strategy
Choose whether to:
- Use off-the-shelf models (like GPT-4 or Claude)
- Fine-tune on your internal data
- Train a new model (rare but possible)
Example:
An e-commerce firm fine-tunes a model on product descriptions and customer reviews to generate personalized recommendations.
Data quality, bias, and intellectual property considerations are paramount here.
3. Prompt Engineering
This is your new “coding.” Prompts control model behavior and output.
Example:
To summarize financial filings, you might prompt:
“Summarize the company’s Q4 performance in 3 bullet points. Highlight revenue trends and risks.”
Expect multiple iterations. The smallest prompt tweak can dramatically change results.
4. Evaluation & Safety
Test not just for correctness—but also:
- Does it hallucinate facts?
- Is it biased or inappropriate?
- Is the tone consistent?
Tools: Red teaming, human-in-the-loop reviews, factuality scoring tools, safety filters.
5. Integration & UX
Incorporate GenAI output into user-facing workflows.
Example:
A real estate CRM adds a “Smart Listing Writer” feature that turns basic property specs into persuasive copy in seconds.
Focus on user feedback, transparency (“AI wrote this”), and fallback mechanisms.
6. Monitoring & Continuous Improvement
GenAI systems don’t age like traditional apps. Models can degrade or become outdated due to:
- Model drift
- Changing user behavior
- New data sources
Regularly monitor performance and retrain or re-prompt as needed.
🎯 What Project Managers Should Keep in Mind
- Expect ambiguity. Success isn’t always measurable in traditional ways.
- Be ready to loop. Prompting and evaluation are iterative.
- Safety & ethics matter. This isn’t just a technical project—it’s also a responsible AI initiative.
✅ Real-World Use Case: GenAI for Loan Processing Automation
At Valstrand, we recently helped a fintech startup reduce loan approval time by 60% by integrating GenAI with their underwriting workflow.
We used:
- OpenAI’s GPT-4 for document summarization
- Custom prompts for risk classification
- A human-in-the-loop review system
Result: Faster approvals, improved compliance, and better customer satisfaction.
📣 Let’s Build Your Next AI-Driven Project
🚀 At Valstrand, we specialize in:
- Project Management for GenAI Implementations
- End-to-End Workflow Automation
- AI Evaluation & Safety Protocols
- Prompt Engineering Strategy
Want to talk through a use case or see how GenAI fits into your business?
👉 Visit us at www.valstrand.com
📧 Contact: vipin.garg@valstrand.com
