You have built a market research bot.

What you have learned

• The boundary between research and trading — what AI can responsibly help with, and what it cannot.
• How to write a system prompt that defines a specialist bot, including the refusal language that holds under pressure.
• How to feed real material — 10-Ks, earnings transcripts, news — and make the bot cite its sources.
• The distinction between pattern recognition (what AI does well) and prediction (what AI does badly), and why the distinction matters in markets specifically.
• The four failure modes of research bots — hallucinated numbers, false confidence, drift from research to recommendation, and the third-party problem.
• A working understanding of the regulatory frame and your personal responsibility within it.

You have built something useful. You have also seen — clearly — where the useful thing ends and where the dangerous version begins. Knowing the line is the entire skill.

What this course did not cover

• Programmatic deployment. A real production research bot uses an API, retrieval-augmented generation against a vector database, and proper versioning. This course was browser-only.
• Quantitative methods. Statistical analysis, time-series, anything that touches numerical modelling of returns. That is a different skill.
• Compliance for institutional use. The frameworks for using AI inside a regulated firm — model risk management, MAR, GDPR Article 22, the EU AI Act high-risk obligations — are their own discipline.
• Real trading. Genuinely and deliberately. If you want to trade, that is a different course taught by a different kind of institution.

Where the Integrated AI Program takes this further

Romeo Advanced Academy's 180-ECTS programme covers the deeper material that sits behind this course.

• Tier 2 — C5: Data Engineering and Pipelines. How research-grade AI systems ingest, normalise, and store source material reliably.
• Tier 2 — C6: Generative AI and Foundation Models. The technical depth behind the LLMs you have been using, including retrieval-augmented generation.
• Tier 2 — C8: AI Governance, Risk and Regulation. The EU AI Act, model risk management, the regulatory frame for any AI system used in financial work.
• Tier 3 Path B — B4: Economics of AI. The cost models and value creation logic of using AI in business and financial settings.
• Tier 3 Path B — B5: AI Due Diligence, Investment and M&A. The professional application of AI in investment analysis — including the limits and the responsible-use principles you have started learning here.
• Tier 3 Path A — A4: MLOps and Production ML Systems. How a research bot moves from a personal tool to a deployed system.

Stay in touch

If you found this course useful, we would love to know. The form below sends a short note to our admissions inbox.

First name

Email

What brought you to the course? I work in finance and want to use AI more rigorously I am an engineer interested in building research tools responsibly I am a consultant or product person thinking about Path B I am a private investor researching companies for my own portfolio I work in compliance, risk, or regulation I am curious about AI applications and chose this one Other Anything you want to tell us? (Optional)

Send Or apply now →

We will only use this to reply to you. We will not subscribe you to a newsletter without asking first.

Three immediate next steps

1. Share the course. If a colleague would find it useful, send them the course link.
2. Take the first course if you haven't. Build Your First AI Agent covers the conceptual foundation this course was built on.
3. Read the prospectus. The full programme prospectus describes how the courses above fit together.