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Retrieval-Augmented Generation (RAG) pipelines have become an integral part of how Large Language Models (LLMs) access information beyond their training cutoff. These pipelines enable LLMs to deliver current, accurate, and grounded responses. By fetching relevant external documents, RAG mitigates common LLM challenges like factual inaccuracies and hallucinations. However, this methodology introduces a new complexity: evaluating RAG pipeline performance is particularly challenging.

CI/CD pipelines are the heartbeat of modern software delivery. They automate builds, run unit tests, package applications, and deploy them to production with a speed that traditional release cycles could never match. For engineering teams under pressure to move fast, pipelines are the mechanism that makes agility possible.

For most teams, the slowest part of delivery isn’t writing code, it’s everything that happens after: automated tests, manual reviews, bug fixes, final approvals, and the long wait for deployment. The longer these phases run, the more expensive and painful late fixes become. As AI makes it easier to generate code at scale, those bottlenecks only get bigger.

An essential part of software development, testing helps catch bugs and errors early, improves software quality, and ultimately prevents costly issues from being deployed to production. The effectiveness of software testing will remain uncertain until it can be measured and that is where code coverage comes in. Code coverage is a metric that tells developers what portion of their codebase is executed when specific tests are run.

Any company still running their own CI/CD, such as Jenkins, is paying a price. The question is which one: Each of these is extremely costly, and yet many software teams still host and run their own CI/CD for two reasons: tools like Jenkins are cheap or free, and hosting Jenkins on an AWS EC2 instance is often cheaper per minute than SaaS CI/CD services like Bitbucket Pipelines. Both reasons are true, but they’re also a trap.

Today’s software organizations can’t make tradeoffs between speed and trust – you need both to succeed. But juggling them is tough. Moving too fast can lead to security vulnerabilities and compliance issues, while moving too slow means your competitors beat you to market. This tension creates friction that slows down every release, a problem that is rooted in your software pipeline.

Email automation allows you to send emails automatically based on certain triggers or schedules, so you don’t have to click the Send button every time. This includes things like welcome messages, drip campaigns, and regular newsletters. In this tutorial, you will create a simple system that automatically welcomes new subscribers and sends them updates about technology, all with the help of AI.

Retrieval-Augmented Generation (RAG) has transformed how applications interact with Large Language Models (LLMs). RAGs ground LLM responses in external knowledge, improves accuracy, and reduces hallucinations. But traditional RAG systems have a significant limitation: they only process text. Multimodal RAG addresses this limitation by processing and understanding multiple data types (text, images, and potentially audio).

Harness GitOps extends Argo CD by preserving its reconciliation loop while adding governance, audit, and RBAC through the GitOps Agent’s secure connection to Harness SaaS. Teams can choose Harness-managed or bring-your-own Argo CD and scale to multi-cluster fleets with unified dashboards, promotion pipelines, and true rollback, while Git stays the single source of truth.