A technical guide to successful prompting

Generated Assets lets you turn any thesis into a fully formed, investable index. Instead of choosing between thousands of off-the-shelf ETFs—each built by someone else, for someone else—you can now build an index that reflects your view of the market.

But like any powerful system, the quality of the output depends on the quality of your input.

This guide walks through how Generated Assets interprets your prompt, how the underlying evaluation agents work, and how to write prompts that produce the most accurate, expressive indices. We’ll also cover how to refine and iterate on your asset using follow-up instructions.

Whether you’re building your first “AI-filtered Nasdaq-100” or exploring something more experimental—like “founder-led companies with improving margins”—this guide will show you how to get the most out of Generated Assets.

Note that this guide is for educational and illustrative purposes only, and the content here should not be viewed as investment advice or recommendations.

How the engine works: The swarm architecture

To write better prompts, it helps to understand what happens when you hit “Generate.” Most consumer AI tools work linearly: You ask a question, and the LLM predicts the next most likely token based on its training data. In finance, this creates a hallucination risk. That’s why we built our architecture to separate intent from evaluation.

1. Intent parsing: When you type a prompt, a primary agent analyzes your text to extract specific financial constraints, sector themes, and exclusion criteria.
2. The evaluation swarm: The system then dispatches a concurrent swarm of evaluation agents. Imagine 5,000 analysts working in parallel. Each agent is assigned specific assets to evaluate against your criteria using real-time and historical market data.
3. Construction and weighting: The results are aggregated, scored for relevance, and constructed into a weighted index.

Because of this architecture, the AI flourishes when you give it specific, multi-factor instructions. It doesn’t just “guess” good stocks; it screens for them based on the logic you provide.

A complete walkthrough: From prompt to evaluation to index

Here is an example of a full prompt, the system’s interpretation, and the resulting index. This mirrors how the swarm architecture works behind the scenes.

Please note that LLMs are stochastic, meaning the system may produce slightly different results each time, especially when a company sits on the edge of your criteria. Behind the scenes, we mitigate this by evaluating multiple passes and averaging the outcomes.

Anatomy of a great prompt

In our early observations, we’ve found many high-quality prompts feature three components:

Example:

Prompt library: Ready-to-use structures

Here are three examples of complete prompts you can copy, paste, and adapt.

Quality + Growth blend

Why it works: It balances growth with discipline, using ROIC and FCF to avoid hype names.

Small-cap value

Why it works: It identifies undervalued operators with improving fundamentals.

Rate-cut scenario

Why it works: It captures a macro theme without blindly overloading on rate-linked sectors.

Common mistakes and how to avoid them

Asking for predictions

Why it fails: The system can only evaluate evidence, not predict outcomes.

Using vibes instead of variables

Why it fails: “Innovation” is too vague. You need to provide measurable proxies:

Over-constraining the problem

Why it fails: An impossible constraint set will lead to an empty universe.

Advanced prompting techniques

For users who want to build sophisticated, multi-factor indices, Generated Assets supports more advanced prompt structures.

Multi-objective optimization

Composite factor definitions

Scenario-based prompts

Hard caps and diversification constraints

Refinement prompts that reshape the thesis

How the system explains its decisions

Unlike black-box AI tools, Generated Assets provides transparent reasoning for each holding.

For every company, the system surfaces:

• Why it was included
• Which constraints it satisfied
• Which signals it aligned with
• How conflicting factors were resolved
• Evidence from fundamentals, historical behavior, and sector linkages

Example

Refining your index: The iterative loop

Once you’ve built an index, the real magic happens in the refinement loop. You can tighten constraints, strengthen signals, or reshape the universe entirely with follow-up prompts:

• “Drop companies with declining EPS.”
• “Narrow results to companies with consistent margin expansion.”
• “Remove the top 5 megacaps to reduce concentration.”

With each iteration, you’re guiding the swarm—like directing a research team toward a sharper interpretation of your thesis.

Final Thoughts

Generated Assets translates your investment thesis into a structured, evidence-driven index. The more clearly you can articulate your intent, the more precisely the system can execute it.

As you experiment with Generated Assets, remember that the system is built to respond to clarity. The more your prompt reflects a coherent investment thesis, the more precisely the swarm can translate it into a structured index.

If you can describe your idea, you can invest in it.