AI Chatbot for the AOE Tech Radar: Tailored Answers to Technology Questions

With growing interest in generative AI, many companies are asking themselves: how can large language models be used meaningfully, securely, and in a targeted way? Our AI chatbot for the AOE TechRadar provides an initial answer – and shows how a specialized bot can offer real value in everyday work.

What is the AOE Tech Radar Chatbot?

The chatbot was developed to quickly, consistently,and transparently answer questions about our Tech Radar. It’s built on a RAG (Retrieval-Augmented Generation) agent architecture, leveraging the capabilities of the connected LLM (GPT-4o in this case) in a controlled environment with a clearly defined knowledge base: all responses are based solely on content from our public Tech Radar repository.

But the bot is more than just a reference tool – it understands the context of the entire conversation, searches relevant entries, and generates structured answers based on that. And it does so without exposing confidential information or competitor names.

The result: faster information access, more targeted exploration of technical topics, and a significantly improved user experience.

Value Through AI – For Users and Teams

Key benefits at a glance:

· Faster access to information: Users no longer have to click through every blip – they ask a question and get a relevant answer right away.

· Interactive content exploration: The bot can explain technologies, make comparisons, and highlight relationships – no endless searching required.

· Increased engagement: It's interactive nature boosts time-on-page and makes the TechRadar more accessible.

· Context-based recommendations: Based on the conversation, the bot suggests related technologies or relevant articles.

· Maintenance & scaling: Frequently asked questions no longer need to be answered manually – the bot can be continuously expanded.

Architecture: How the Bot Works

The technical implementation is based on a modular architecture focused on performance, traceability, and scalability:

Frontend (React):

·        Real-time streaming of answers for seamless interaction

·        Automatic formatting of results in Markdown for better readability

Backend (Python + LlamaIndex):

·        Creation of a vector index based on ratings and content from the TechRadar repository

·        Combines retrieval and generation: the bot searches the index and otherconnected tools for relevant content and passes it, with context, to the LLM(GPT-4o) for response generation

·        Includes prompt injection detection to ensure the bot only answers TechRadar–related questions

Quality & Monitoring:

·        Integrated testing and evaluation pipeline for continuous qualityassurance and answer improvement

·        Use of tracing and observability tools to analyze component interactionsduring runtime

·        Monitoring of token usage during the internal testing phase to optimizecost and performance

 Have a look at: https://techradar.aoe.com/

Conclusion

Our AI chatbot demonstrates how generative AI can be applied in a targeted way to specificinformation domains. With a clearly defined knowledge base, technicalsafeguards, and strong infrastructure in place, it presents a real-world usecase – currently for internal use, and potentially beyond in the future.

Interested in how a similar chatbot could be built for your organization? Let’s talk – we think about architecture, UX, and AI as a whole.