My Generative AI Development Lifecycle

Over the past two years, my development lifecycle for generative AI solutions has taken shape, evolving from chaotic experimentation into a repeatable, reliable blueprint. What started as trial and error has matured into a structured loop I now rely on to build, ship, and continuously improve AI features that work in the real world. This lifecycle isn't theoretical; it's built from real deployments, production failures, and tight feedback loops. Here's how it works, step by step:

1. Write the Prompt (strategically)

Despite the name, this step is never just about writing a single prompt.

It's where the original architecture happens, designing the entire prompting strategy. I decide whether the task calls for one-shot prompting, chain-of-thought reasoning, structured outputs, or tool use through function calling. This step is where I decide whether to use some form of RAG, to apply fine-tuning, or just rely on the model’s context window. Sometimes it means orchestrating multiple agents in parallel, each handling a piece of the puzzle. Other times, it's about mixing approaches, a bit of structure here, a free-form fallback there.

This step is where I turn ambiguity into a plan: what do I want the model to do, how should it reason, and what should the output look like? The rest of the lifecycle depends on how well I do this.

2. Evaluate (and evaluate again)

No prompt survives contact with production without solid evaluation.

I use Promptfoo as my go-to framework for testing and refining the approach I took in Step 1. This is where I pit different prompts, models, and providers against each other using automated evaluations, both deterministic (string matches, regex, structure checks, etc.) and model-graded metrics (G-eval, context relevance, LLM rubrics, etc.).

The goal? Confidence. I want to know before I ship that I've chosen the best-performing option across latency, cost, accuracy, and quality.

3. Deploy the AI (for Real Users)

Deployment is where everything gets real.

This is where I integrate the AI feature into the full-stack application, ensuring it works seamlessly with the rest of the system. I usually operate in a serverless environment, so this means setting up Lambda functions (or other serverless functions) with the proper memory, timeouts, and configuration to handle AI inference workloads.

For longer-running jobs, I offload to async queues. And to keep the user experience responsive, I rely on streaming so the frontend stays in sync with real-time progress.

4. Observe the AI in Production (LLM Observability)

Once deployed, I shift to watching closely.

LLM behavior in production is full of surprises, so I use observability tools like Datadog LLM Observability or Langfuse to track what's happening under the hood. I monitor inputs, outputs, latency, errors, cost, and quality metrics, and most importantly, set up alerts to notify the team if anything goes off track.

This step turns AI from a black box into a glass box; we see what's working, what's failing, and what needs attention.

5. Revisit & Improve

This is where the real learning happens.

After going live, edge cases show up. User feedback rolls in. Costs fluctuate. Model behaviors shift with updates. So I loop back, revisit the prompt, and apply everything I've learned from observability and human feedback.

Sometimes it's tweaking how the model is used. Other times, it's reworking the entire prompt strategy or switching providers. The key is: I don't treat AI features as done after deployment; they evolve, and this step ensures they keep getting better.

Final Thoughts

This development lifecycle is how I ship AI that works, not just in demos, but in production. It's fast, repeatable, and grounded in continuous feedback. Every step builds on the last, and the loop never ends because the best AI features are the ones that keep learning.

I'm always curious about the process other AI Engineers follow. Am I missing something? Please share your thoughts! Thank you 😊