Understanding the Evolution: From Classic Chatbots to RAG Chatbots to AI-Powered Assistants


With the launch of ChatGPT in late 2022 and the corresponding rise of generative artificial intelligence (gen-AI), the landscape of conversational agents significantly evolved. While the chat interface may appear the same as in past years, new technologies have dramatically expanded its capabilities.
The security industry is rapidly integrating chat systems to streamline operations, from automating customer service interactions to managing physical access control; however, deploying these conversational agents effectively requires a nuanced understanding of the capabilities and vulnerabilities of the three primary types now in use.
Classic Chatbots
Classic chatbots are the earliest form of conversational agents, operating primarily through predefined scripts and decision trees. They rely on pattern matching and keyword recognition to generate responses.
Benefits of classic chatbots
- Simplicity: They are easy to configure; a software engineering background is not needed to develop one.
- Consistency: With a classic chatbot, you can control its outputs; this can be critical in the security industry if chatbot mistakes could lead to physical safety concerns.
- Vendor support: There are many vendors that can provide frameworks to allow for the building of these bots.
Considerations for using classic chatbots
- Lack of natural conversation: They do not possess the same conversational feel of gen-AI-powered experiences because they utilize pattern matching on keywords and phrases.
- Limited understanding: They struggle with understanding context and the nuances of human language, making them less effective for complex queries.
Key impacts for the security industry
- Tier 0 customer support: Classic chatbots are best suited for simple, repetitive tasks like answering basic customer service questions.
- Alternative to gen-AI: They are a good alternative when the use of gen-AI could compromise physical safety.
Retrieval-Augmented Generation (RAG) Chatbots
RAG chatbots combine advanced search engine capabilities and generative models, allowing for more contextually relevant interactions. They can quickly search across many documents, finding relevant passages that they then provide to a large language model to use as context when answering a question.
Benefits of RAG chatbots
- Easy setup: Major providers such as Anthropic, Google and OpenAI offer templates to create RAG chatbots on top of a document library; this provides the experience of a highly customized chatbot without requiring any custom model training.
- Contextual understanding: The ability to have contextual understanding, meaning it can handle complicated questions or instructions and provide intelligent responses
Considerations for using RAG chatbots
- Informational responses: RAG systems only provide instructional or informational responses; they do not perform actions within applications.
- Prompt engineering: They can be manipulated by bad actors to produce unintended results; prompt engineering and thorough testing are essential.
- Required skills: Software engineering skills are needed to deploy a production RAG system.
- Knowledge management: RAG systems are only as good as the information provided to them; knowledge management best practices are needed to make the systems scalable.
Key impacts for the security industry
- Documentation navigation: RAG systems can make complex documentation more understandable, aiding in security product installation and user guides.
- Testing: Thorough testing is necessary to ensure the system does not provide incorrect information that could result in liability.
AI-Powered Assistants
AI-powered assistants represent the most advanced stage of conversational agents. Like RAG chatbots, AI-powered assistants can retrieve and generate information, but they are also capable of performing complex, multistep interactions on behalf of a user.
Benefits of AI-powered assistants
- Advanced understanding: These assistants maintain context over long conversations and understand nuances, making interactions more natural.
- Action-oriented: They can perform actions within software applications based on natural language instructions, enhancing user efficiency.
Considerations for using AI-powered assistants
- Complexity and cost: They are more complex to develop and maintain compared to classic and RAG chatbots.
- Integration: They require integration with existing software applications to perform actions, which may involve a significant development effort.
Key impacts for the security industry
- Safety: Extensive testing should occur if these assistants are performing actions that could have an impact on physical safety, such as silencing security alerts or granting access to unauthorized individuals.
- Scope of access: AI assistants should be carefully vetted for the scope of their access within a software application.
- User awareness: It is crucial for users to understand what an AI assistant is and is not capable of, in order to set realistic expectations and avoid potential misuse.
Overall, classic chatbots are great for straightforward tasks, RAG chatbots offer a balanced approach for nuanced interactions, and AI-powered assistants provide unparalleled sophistication and versatility, including the ability to perform actions within software applications.
For the security industry, these advances in chatbots present both opportunities and challenges. The ability to automate and enhance security operations can lead to improved efficiency and better threat management; however, it is crucial to balance these benefits with the need for rigorous testing, ethical considerations and user education to mitigate potential risks.
The views and opinions expressed in guest posts and/or profiles are those of the authors or sources and do not necessarily reflect the official policy or position of the Security Industry Association.
This article originally appeared in All Things AI, a newsletter presented by the SIA AI Advisory Board.