A GPT-style chatbot that is trained on a specific industry's public content, such as podcasts, blogs, and newsletters. It uses Retrieval-Augmented Generation (RAG) to provide detailed answers grounded in expert content.
Useful expertise in many fields is spread across hundreds of hours of podcasts, years of blog archives and newsletter back-issues. Someone with a specific question, such as how a superannuation contribution cap interacts with a salary sacrifice arrangement, has to guess which episode might cover it and then listen or skim for the relevant part. Content creators and firms that publish this material know it contains answers, but it is practically unsearchable once it is more than a few weeks old.
A generic chatbot does not solve the problem. It answers from its general training, may be out of date, cannot say which expert said what, and sometimes invents plausible detail. For a business wanting to offer a trusted question-and-answer experience, that is a liability. What is needed is a system that only answers from a defined, vetted body of content, shows where each point came from, and admits when the library does not cover the question.
Approved sources are collected: your own episodes and articles, plus third-party content you have permission to use, pulled in through RSS feeds, APIs or uploaded files.
Audio is transcribed, text is cleaned and split into passages, and each passage is embedded and stored in a vector database with metadata such as author, date and episode.
When a user asks a question, the system retrieves the most relevant passages from across the library and passes them to an LLM such as ChatGPT or Claude.
The model writes an answer using only those passages, cites the episodes or articles it drew on, and states plainly when the sources do not cover the topic.
A scheduled job ingests new episodes and posts, while an editor reviews flagged answers and removes outdated material from the index.
A financial advisory firm creates a chatbot trained on the top 50 finance podcasts. Users can ask complex questions like 'What are the pros and cons of a backdoor Roth IRA conversion?' and receive a detailed, accurate answer synthesized from multiple expert sources.
RAG, Vector Databases, Podcast/Blog Scraping APIs, LLMs (GPT, Claude)
Scraping other people's podcasts and articles to power a commercial product may infringe copyright under the Copyright Act 1968 and usually breaches the publisher's terms, so build on your own content or get written permission or a licence. If the topic is finance, answers can amount to general financial product advice, which is regulated by ASIC and generally requires an Australian Financial Services Licence or authorisation plus a general advice warning. Many overseas podcasts discuss US rules that do not apply here, so tag sources by jurisdiction. Question logs are personal information under the Privacy Act 1988 when linked to a user.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
It is risky. Transcribing and reusing someone else's episodes in a commercial product may infringe their copyright and usually conflicts with platform terms. The safer approach is to use your own content, partner with creators under a written agreement, or license a content library. Linking and attributing does not by itself give you permission to copy the material.
Accuracy depends mostly on the quality and currency of the library rather than the model. When relevant passages exist, answers are usually well grounded and cited. Problems arise when sources disagree, are out of date or only touch on the topic. Testing with a set of known questions before launch shows where the library is thin.
Collecting and clearing rights to content, transcribing audio, and tuning how passages are split and retrieved take most of the effort. The chat interface itself is quick. Building a test set of real questions and having an expert grade the answers is the step that most improves quality and should not be skipped.
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