Welcome to part three of our series on using Temporal to improve the reliability of applications built around LLMs like the one that powers ChatGPT. In part one, you learned how to use Temporal to clone a repo and ingest its documentation into an RAG Database for use with your LLM. Part two taught you how to use context injection to give users more accurate answers to prompts made against that documentation. In this post, you’ll use Temporal and another LLM to automatically test the accuracy of your application’s answers to the prompts from part two.

Throughout my DevOps consulting career, I've realized that the true value of our work lies in optimizing processes to free up developer time. A key part of optimizing DevOps processes is understanding the difference between manual (on-demand) and automated (triggered) processes.

In this post, we’ll dive into both manual and automated triggers, exploring their roles and integration in CI/CD pipelines. We’ll also tackle the differences between Continuous Delivery and Continuous Deployment, and why one might be a more realistic goal than the other. By understanding these mechanisms and exploring automation opportunities, you can streamline workflows, reduce errors, and boost productivity in your development process.

Continuous Integration (CI) and Continuous Delivery & Deployment (CD) are pivotal concepts in modern software development, facilitating rapid and reliable delivery. By frequently integrating code into a shared repository, detecting issues early, and automating deployments, CI/CD ensures that software can consistently be released to production with high confidence.

In the fast-paced world of DevOps, it’s challenging to produce and test high-quality software without sacrificing efficiency. But what if we told you there’s a better way?

Welcome to part two of our series about using Temporal to improve the reliability of applications built around Large Language Models (LLM) like the one that powers ChatGPT. Part one explained how to build a Temporal Workflow to process a series of documents and make them accessible to your LLM. This post will show how to develop a Temporal Workflow to find documents relevant to a user’s query and supply them as context to a prompt sent to the LLM using Context Injection. You’ll also learn how Temporal's abstraction will make your application more reliable and make it easier for you to extend it with new features.

Taking advantage of the burgeoning AI trend, many of today's applications are built around AI tools like ChatGPT and other Large Language Models (LLMs). AI-optimized applications often have complex software pipelines for collecting and processing data with the LLM. Temporal provides an abstraction that can significantly simplify data pipelines, making them more reliable and accessible to develop. In this post, you’ll discover why you should use Temporal to build applications around LLMs.

Last week, Bitovi Hatchify officially left the nest! Hatchify is a suite of low-code libraries that provides a powerful scaffold to build CRUD-heavy applications, eliminate boilerplate, and enable your team to focus on domain-specific functionality from day one. Hatchify empowers you to build admin views, dashboards, and B2B apps faster, all while retaining uncompromising flexibility.