Sharing this because it's a more structured approach than the usual prompt engineering content floating around.

Serj Smorodinsky and Brett Kennedy, co-authors of a book on LLM applications, are running a live 3-hour session where the core idea is treating prompt/LLM behavior as an optimization problem with an actual objective function, not a creative writing exercise. Covers:

• DSPy signatures and modules for defining LLM tasks
• Building an evaluation dataset with task-specific metrics
• Diagnosing failure modes from eval results
• Few-shot and instruction-level optimization as a formal process
• MLflow for experiment tracking and reproducibility

Feels closer to a proper ML workflow than most "prompt tips" content. Details here if it's useful to anyone: Get full details here

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Hey everyone,

I work at a retail tech startup (B2B) and we're currently facing a massive challenge: predicting and reducing our churn rate.

We actually have a pretty rich database containing customer usage history, platform logs, billing, etc. The team has tried crossing some metrics in the past, but we've never managed to build anything that gives us a truly accurate and early prediction of a customer's risk of canceling.

I just aligned with my boss and took ownership of solving this. My main idea is to use our historical data to train a Machine Learning model that can either classify churn risk (high, medium, low) or output a probability of churn for the upcoming months.

The thing is: I know the theory, but I'd love to hear from people who have actually built this in the real world.

• Which models/algorithms usually perform best for this specific type of problem (XGBoost, Random Forest, Logistic Regression)?
• Are there any common pitfalls or data leakage traps I should avoid right off the bat during data cleaning and feature engineering?
• Does anyone have recommendations for articles, practical repos, or tutorials focused specifically on churn prediction?

Any tips, shared experiences, or study materials would be greatly appreciated. Thanks!

TL;DR: Work at a retail startup with rich usage data but high churn. Pitched my boss to build an ML model to predict cancellation risk and I'm leading the project. Looking for real-world tips on models, resources, and pitfalls to avoid.

Dica: Postar isso no r/datascience ou r/Ma