Last November over 80 technical communicators, information architects, and documentation aficionados broadened their horizon at GrowWithFlow, Flow’s highly irregular but equally appreciated technical communication conference. Here is one of the highlights: GenAI and RAGs in technical documentation.
Chatbots are a great tool to help users find information quickly. They’re often the first representative of a company that the user ‘speaks’ with. So how do you make sure the chatbot does a good job representing you and your company?
Imagination without limits
Generative AI (GenAI) chatbots are powered by large language models (LLMs); advanced systems trained on large datasets to generate realistic and coherent text. These models excel at language tasks, enabling natural conversation and creative text generation.
However, they’re not always equally reliable as they use sources that may include outdated or incorrect information. Additionally, sometimes LLMs can outright hallucinate, producing details that sound plausible but are entirely invented.
Reliable creativity through RAG
How do you get the best out of your chatbot so it gives answers you can trust? This is where retrieval-augmented generation (RAG) comes in. RAG is a method that makes AI answers both contextually rich and factually reliable.
This method answers the question about reliability by allowing you to define your trusted content sources, such as domain-specific knowledge bases. RAG ensures that the chatbot only provides information from these sources and does not hallucinate a likely answer when it cannot find what it needs in the source.
In addition to defining what it answers, you can also guide the how by adding custom instructions in an initial prompt. For example:
Describe how the chatbot should match the company’s tone of voice
Define the specific company terminology the chatbot should use
Tell the chatbot to look in specific sources depending on the type of question the user asks
At GrowWithFlow 2024, we delved into the mechanics of RAG, showcasing its potential to transform AI applications. This included a live demonstration of a RAG-powered chatbot that provided precise, context-aware answers tailored to specific domains. The demonstration illustrated not just the theoretical benefits of RAG but also its practical application in enhancing the reliability and usability of AI systems.
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