# Tutorials

- [Instructions](https://docs.dbnl.com/v0.25.x/tutorials/instructions.md): How to navigate the Tutorials section of the documentation
- [Trading Strategy](https://docs.dbnl.com/v0.25.x/tutorials/trading-strategy.md): The following tutorial will help you understand the basics of how to continuously test a multi-component system involving components owned by third-parties.
- [LLM Text Summarization](https://docs.dbnl.com/v0.25.x/tutorials/llm-text-summarization.md): In this advanced tutorial, we demonstrate how to use dbnl to automatically evaluate the consistency of summarization output on a fixed set of documents.
- [Setting the Scene](https://docs.dbnl.com/v0.25.x/tutorials/llm-text-summarization/setting-the-scene.md): These are the objects and compute resources used to design this summarization app.
- [Prompt Engineering](https://docs.dbnl.com/v0.25.x/tutorials/llm-text-summarization/prompt-engineering.md): We execute a king-of-the-hill constrained optimization process to identify a high performing, suitably coherent summarization app.
- [Integration testing for text summarization](https://docs.dbnl.com/v0.25.x/tutorials/llm-text-summarization/integration-testing-for-text-summarization.md): After our summarization app is deployed, we conduct nightly integration tests to confirm its continued acceptable behavior.
- [Practical considerations](https://docs.dbnl.com/v0.25.x/tutorials/llm-text-summarization/practical-considerations.md): Our tutorial focuses on the minimum factors required to facilitate testing, but here we discuss the complexity of an actual process.
- [Predicting Credit Worthiness Using Tabular Data](https://docs.dbnl.com/v0.25.x/tutorials/predicting-credit-worthiness-using-tabular-data.md): This tutorial provides a comprehensive understanding of dbnl within the scope of tabular data. It guides you through the process of continuous testing of third-party endpoints.


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