u/New_Championship3929

I'm trying to build a checklist of everything teams should think about when launching an AI product into production.

Beyond the model itself, what are the key systems and practices you consider essential before launch and after launch?

Some things that come to mind:

Prompt versioning

Traceability

Observability

Evals (offline and online)

Human feedback loops

Guardrails and safety

Latency and cost monitoring

Hallucination detection

A/B testing

Model versioning and rollback

Analytics and user behavior tracking

What am I missing?

I'd love to hear from teams shipping LLM products in production:

What tools or platforms do you use?

What mistakes did you make on your first launch?

What metrics do you monitor daily?

If you had to build an AI product from scratch again, what would you do differently?

Looking forward to insights from PM who shipped multiple Ai products?

I'm trying to build a checklist of everything teams should think about when launching an AI product into production.

Beyond the model itself, what are the key systems and practices you consider essential before launch and after launch?

Some things that come to mind:

Prompt versioning

Traceability

Observability

Evals (offline and online)

Human feedback loops

Guardrails and safety

Latency and cost monitoring

Hallucination detection

A/B testing

Model versioning and rollback

Analytics and user behavior tracking

What am I missing?

I'd love to hear from teams shipping LLM products in production:

What tools or platforms do you use?

What mistakes did you make on your first launch?

What metrics do you monitor daily?

If you had to build an AI product from scratch again, what would you do differently?

Looking forward to insights from PM who shipped multiple Ai products?

I'm trying to build a checklist of everything teams should think about when launching an AI product into production.

Beyond the model itself, what are the key systems and practices you consider essential before launch and after launch?

Some things that come to mind:

Prompt versioning

Traceability

Observability

Evals (offline and online)

Human feedback loops

Guardrails and safety

Latency and cost monitoring

Hallucination detection

A/B testing

Model versioning and rollback

Analytics and user behavior tracking

What am I missing?

I'd love to hear from teams shipping LLM products in production:

What tools or platforms do you use?

What mistakes did you make on your first launch?

What metrics do you monitor daily?

If you had to build an AI product from scratch again, what would you do differently?

Looking forward to insights from PM who shipped multiple Ai products?

What are the best approches to grade genie supervisor agent responses or data pulling should I directly provide feedback in the chat or MLOps? What should be the best grading criteria's?

Hey everyone, I recently joined a tech startup as an Implementation/Technical Project Manager and I want to improve our internal processes and implementation processes so team can be more productive. Our implementations are complex — typically 3-6 months per enterprise client, multiple phases, several third party integrations, and heavy dependency on client-owned tasks. What kind of automations, agentic ai internal tooling should I build? It also needs to have better reporting

I would love to hear if anyone is in similar boat and what worked out for you guys?

Hey everyone, I recently joined a tech startup as an Implementation/Technical Project Manager and I want to improve our internal processes and implementation processes so team can be more productive. Our implementations are complex — typically 3-6 months per enterprise client, multiple phases, several third party integrations, and heavy dependency on client-owned tasks. What kind of automations, agentic ai internal tooling should I build? It also needs to have better reporting

I would love to hear if anyone is in similar boat and what worked out for you guys?

Hey everyone, I recently joined a tech startup as an Implementation/Technical Project Manager and I want to improve our internal processes and implementation processes so team can be more productive. Our implementations are complex — typically 3-6 months per enterprise client, multiple phases, several third party integrations, and heavy dependency on client-owned tasks. What kind of automations, agentic ai internal tooling should I build? It also needs to have better reporting

I would love to hear if anyone is in similar boat and what worked out for you guys?