The Magic of Building a Hybrid RAG AI Assistant for Automation Certification

The aura of technological evolution

The evolution of automation and artificial intelligence (AI) is unfolding before our very eyes. What an exciting time to be an engineer! Many of us who grew up with digital technology can look back with a sense of nostalgia at how magical it felt, especially in its early stages.

Those of us who came of age in the early days of the Internet, before the convenience of plug-and-play, also remember the struggles of that era-waiting for the phone line to clear before logging on through the loud hiss of a dial-up modem, juggling cases full of CDs and stacks of floppy disks, and enduring the inevitable crashes, where not only were connections lost, but data was wiped out completely.

Within a decade or so, the modern technical world leapt from dial-up to broadband and Wi-Fi, from floppy disks to microSD cards and USB drives, and from magnetic tapes and spinning disks to solid-state storage.

The era of automation and AI has greatly benefited from the groundwork laid by engineers in previous decades, resulting in a short runway to liftoff that has been exhilarating. Yet it can leave some feeling uncertain about the speed and direction of innovation. We might wonder how to make the most of this unprecedented opportunity.

Artificial intelligence and machine learning

Machine learning (ML) is a subset of AI. It enables machines to be trained to recognize patterns, predict outcomes, and generate results with remarkable efficiency. And it has already been in use for decades-often unnoticed-in the form of search recommendations powered by data science techniques such as clustering, classification, and regression, along with advanced algorithms.

Until recently, most engineers and users didn't need to understand the basics behind ML technology implementation, including cornerstones like natural language processing and newer innovations, such as generative AI. That time has passed.

Fortunately, the rapid progression of AI means the last days of the awkward, experimental phase. AI-augmented biometrics, chat assistants, and other daily utilities, such as automated note-taking during meetings, are accessible to the general public, impacting an ever-growing segment of society.

Engineers must now seize the opportunity to leverage equally powerful domain-specific tools, such as AI-enabled integrated development environments (IDEs). Yet, even these tools can frustrate engineers with issues such as hallucinated responses (irrelevant or incorrect outputs) and unreliable code suggestions stemming from poor training data.

So how does an engineer navigate the expanding field of models, neural networks, algorithms, and prompts? Is there a plug-and-play approach to harnessing AI effectively for network engineering, DevOps, cloud, and beyond?

Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open standard that enables seamless interaction between large language models (LLMs) and external tools, systems, and data sources. Think of MCP as the USB-C of AI tooling-a universal, model-agnostic interface for tasks like loading and reading data, executing commands, and handling contextual prompts.

A key advantage is that MCP abstracts away the complexity of building and maintaining custom connectors for every tool or data source, making integration with LLMs dramatically simpler.

If you're proficient with APIs and want to easily progress to leveraging MCP, check out Wrangling the Wild West of MCP Servers, by Kareem Iskander, Head of Technical Advocacy. In this blog, he demonstrates how to convert an OpenAPI specification into a FastMCP server using just your spec and a few lines of code-no custom scaffolding required.

Retrieval-Augmented Generation (RAG)

Before MCP gained momentum, the dominant approach to improving LLM accuracy was Retrieval-Augmented Generation (RAG). RAG is a method of injecting domain-specific context (such as documents, websites, and structured or semi-structured data) into an LLM to reduce hallucinations and improve accuracy.

A general-purpose, generative, pre-trained transformer (GPT) model, while trained on vast knowledge, simply can't match the precision of an LLM enhanced by RAG. That's why RAG remains a foundational technique for many real-world AI applications.

From an AI engineering perspective, the real breakthrough of RAG is that its effective implementation often allows you to sidestep the high cost and complexity of fine-tuning large models.

Hybrid RAG

The next evolution of this concept is Hybrid RAG, a special type of RAG-augmented LLM that combines multiple retrieval sources. This system contains a logical controller that determines when to retrieve domain-specific knowledge from a vector database containing embeddings of your documents and websites, and when to query external sources, such as the Internet. The Hybrid RAG then merges the best of both results into a unified, context-rich response. This approach strikes a balance between precision and breadth, offering both specialized and general intelligence.

They say the best way to learn something is to do it. So, in my effort to understand Hybrid RAG at a deep level, I built one from scratch. The beauty of this project lies in my use of automation and AI to learn, demonstrate, and empower others in the realm of automation and AI.

The Cisco Automation Certification Station

The shift from Cisco DevNet certifications to Cisco Automation certifications, which becomes final on February 3, 2026, marks an important transition. As technical advocates in Learn with Cisco, it is our duty to dispel confusion and enable technical professionals and students to succeed.

In that spirit, Technical Advocate Quinn Snyder's blog, CCNP Automation: A Renamed Certification, Reimagined, expertly breaks down how Cisco DevNet certifications are evolving. Beyond its new name (Cisco Automation certifications), highlighting the new approach to testing, the increased focus on automation, and the incorporation of AI into the exam and certification structure.

This certification change has presented an exciting opportunity for me to design the Cisco Automation Certification Station, a proof-of-concept learning tool in the form of a single-pane webpage, specifically tailored to domain-specific automation certification and knowledge.

The Cisco Automation Certification Station is a production-ready Hybrid RAG system designed to streamline and supercharge prep for Cisco Automation certification candidates. It combines local document search, web search, and AI generation to provide comprehensive, source-backed answers for all Cisco Automation and Cisco DevNet certifications, as well as automation in general. Think of the Cisco Automation Certification Station as an intelligent, always-available study partner with deep, expert knowledge in the automation domain.

By leveraging a robust knowledge base built from actual Cisco exam blueprints, documentation, websites, and other resources and integrating advanced AI capabilities via Google Gemini, this Hybrid RAG provides accurate, relevant, and instantaneous answers to complex technical queries. It doesn't just pull information; it understands the context of the question and synthesizes relevant details, much like a seasoned expert would.

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The Cisco Automation Certification Station is now live at cs.co/automation-certification-station, providing technical answers to specific exam topics and automation in general, with source-backed responses and study plans, while maintaining context for follow-up questions.

Benefits of the Cisco Automation Certification Station include:

• Clarity for the certification community: This tool provides a cutting-edge, interactive way for learners to understand the new Cisco Automation certifications and the transition from Cisco DevNet certifications.
• Improved exam preparation: By providing precise, on-demand information, this tool empowers learners to grasp complex concepts, making the learning experience less painful and even enjoyable.
• Showcase of innovation: This project demonstrates Learn with Cisco's commitment to leveraging the latest AI technologies to deliver superior educational tools, reinforcing Cisco's position as a leader in technology and learning innovation.
• Scalability and adaptability: The underlying RAG architecture is highly adaptable. While currently focused on automation, this framework can be easily adjusted to support whatever an organization needs, creating a versatile asset for any technical portfolio -a reproducible and useful domain-knowledge-specific tool for both external and internal use.

To take a look under the hood and see how the Cisco Automation Certification Station was built, you can visit the GitHub repo.

Call to action

Hopefully, this project will spark some magic in your knowledge quest and guide or motivate you to achieve your technical goals, whether that's earning a Cisco Automation certification or just building your understanding and proficiency in the realm of AI and automation.

If you or someone you know has seen their automation learning stall or is feeling hesitant about the inevitable changes AI will bring to connectivity and processing in our world, it's not too late.

Enter Learn with Cisco-the ultimate learning source for all things Cisco (and much more):

• Are you just getting started in tech or need to refresh your knowledge and skills? Visit the Cisco Networking Academy.
• Are you interested in curated learning paths, courses, and tutorials to take your tech skills to the next level? Check out Cisco U.
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