Unlocking Unicorn Ideas: Why Studying AI System Prompts is the New Competitive Edge

In the fast-paced world of artificial intelligence, where billion-dollar valuations seem to emerge overnight, the search for the next 'unicorn' idea is relentless. Entrepreneurs and investors alike scour market trends, technological breakthroughs, and consumer needs, hoping to pinpoint the next disruptive innovation. But what if the blueprints for these future successes aren't hidden in plain sight, but rather embedded within the very tools that power today's AI giants?

Brad Menezes, the insightful CEO of enterprise 'vibe coding' startup Superblocks, champions this intriguing perspective. He believes that the next crop of billion-dollar startup ideas are not some entirely novel concept waiting to be discovered from scratch, but are instead hiding in plain sight within the intricate system prompts used by existing, successful AI startups. This isn't about copying; it's about learning from the masters of prompt engineering and understanding the underlying mechanics of successful AI applications.

System prompts are the often lengthy, detailed instructions — sometimes exceeding 5,000 or 6,000 words — that AI companies provide to foundational models from providers like OpenAI or Anthropic. These prompts are designed to guide the large language models (LLMs) on how to behave, what tasks to perform, and how to interact to deliver the specific application-level AI products users experience. In Menezes' view, these prompts are akin to a master class in the practical application of prompt engineering, refined through countless iterations and real-world usage.

"Every single company has a completely different system prompt for the same [foundational] model," Menezes explained in an interview. "They're trying to get the model to do exactly what's required for a specific domain, specific tasks." This variation, driven by the unique needs and goals of each AI product, is precisely what makes them so valuable to study. They represent distilled knowledge about how to coax complex behaviors out of general-purpose AI models for specific, commercially viable purposes.

While not always immediately obvious, system prompts are often accessible. Many AI tools can be prompted by users to reveal their underlying instructions. However, they aren't always publicly documented or easy to find, making a curated collection particularly valuable.

Recognizing this potential goldmine of information, Superblocks made a strategic move as part of the announcement for their new enterprise coding AI agent, Clark. They offered to share a file containing 19 system prompts from popular AI coding products such as Windsurf, Manus, Cursor, Lovable, and Bolt. This initiative quickly gained traction, with Menezes' tweet about the collection going viral, garnering millions of views and attention from prominent figures in the tech industry, including investors and startup veterans.

The timing of this initiative coincided with significant news for Superblocks itself. The company recently announced a $23 million Series A extension round, bringing their total Series A funding to $60 million. This substantial investment underscores the market's belief in Superblocks' mission to enable non-developers within enterprises to build and maintain internal applications using 'vibe coding' tools — a concept deeply intertwined with effective AI agent development.

Given the buzz around studying system prompts, we delved deeper with Menezes to understand his perspective and learn how aspiring entrepreneurs and AI developers can extract meaningful insights from these detailed instructions.

Beyond the Prompt: The 80% Secret Sauce

One of the most critical lessons Menezes and his team learned while building Clark and analyzing various system prompts is that the prompt itself, while essential, is only a piece of the puzzle. "I'd say the biggest learning for us building Clark and reading through the system prompts is that the system prompt itself is maybe 20% of the secret sauce," Menezes revealed. The system prompt provides the foundational LLM with its core identity and baseline instructions for the task at hand.

The remaining 80% — the true differentiator and often the source of competitive advantage — lies in what Menezes calls "prompt enrichment" and the surrounding infrastructure. This encompasses everything a startup builds *around* the calls to the LLM. It includes:

**Pre-processing:** How user input is analyzed and potentially augmented before being sent to the LLM.
**Dynamic Context Injection:** Adding relevant information (like user history, specific data points, or external knowledge) to the prompt based on the user's query and the current state. This is often where techniques like Retrieval Augmented Generation (RAG) come into play, allowing the AI to access and reference external, up-to-date, or proprietary information.
**Tooling and API Integration:** Defining and enabling the LLM to use external tools, APIs, or internal systems to perform actions beyond just generating text (e.g., searching databases, executing code, sending emails, interacting with enterprise software).
**Post-processing and Validation:** How the LLM's output is handled after it's generated. This can involve checking for accuracy, formatting the response, filtering out undesirable content, or triggering subsequent actions.
**Guardrails and Safety Mechanisms:** Implementing layers to ensure the AI behaves within defined parameters, adheres to safety guidelines, and avoids generating harmful or inappropriate content.
**Iterative Refinement:** The ongoing process of analyzing AI performance, user feedback, and prompt effectiveness to continuously improve both the system prompt and the surrounding infrastructure.

Studying system prompts provides clues about the *intended* behavior and capabilities of an AI product, but understanding the full 'secret sauce' requires inferring or investigating the infrastructure that supports it. The prompt tells you *what* the AI is instructed to do; the infrastructure determines *how effectively* it can do it, *what resources* it can access, and *how reliably* it performs in real-world scenarios.

Deconstructing the Prompt: Roles, Context, and Tools

Menezes highlighted three key components within system prompts that are particularly insightful to study:

**Role Prompting:** This involves assigning a specific persona or identity to the LLM. It helps the model maintain consistency in tone, style, and perspective. While written in natural language, these role descriptions are often exceptionally specific and detailed. Menezes emphasized the need to "speak as if you would to a human co-worker," stressing that the instructions must be perfect and unambiguous for the AI to reliably adopt the desired role. A famous example comes from Devin, an AI software engineer, whose prompt begins, "You are Devin, a software engineer using a real computer operating system. You are a real code-wiz: few programmers are as talented as you at understanding codebases, writing functional and clean code, and iterating on your changes until they are correct." This sets a clear expectation for Devin's capabilities and operational environment.
**Contextual Prompting:** This provides the LLM with the necessary background information and constraints it needs to consider before generating a response or taking action. Contextual prompts establish guardrails, clarify objectives, and define limitations. They can be used to manage costs (e.g., by limiting the scope of searches or generations), ensure clarity on complex tasks, or enforce specific output formats. Cursor, an AI coding assistant, provides a good illustration with instructions like, "Only call tools when needed, and never mention tool names to the user — just describe what you're doing. ... don't show code unless asked. ... Read relevant file content before editing and fix clear errors, but don't guess or loop fixes more than three times." These instructions define how the AI should interact with the user and handle common coding tasks, preventing common pitfalls like exposing internal tool names or getting stuck in correction loops.
**Tool Use:** This is perhaps the most critical component for enabling AI agents to perform complex, multi-step tasks. Tool use instructions teach the LLM how to interact with external functions, APIs, or systems. By defining available tools (like code interpreters, database query engines, web browsers, or enterprise application interfaces) and explaining how to use them, the prompt empowers the AI to go beyond simple text generation and perform actions in the real or digital world. Replit's system prompt, for instance, is described as lengthy and detailed, outlining how the AI can perform actions such as editing and searching code, installing programming languages, setting up and querying databases (like PostgreSQL), and executing shell commands. This level of detail is necessary for the AI to effectively leverage these external capabilities to assist users with complex development workflows.

By dissecting how different AI products implement these three components, one can gain a deep understanding of their core functionality, their strengths, and their limitations. This analysis can reveal patterns in effective prompt design and highlight areas where existing tools may fall short, pointing towards potential opportunities for new products.

Identifying Market Gaps and Building New Products

Studying the system prompts of various AI coding tools provided Menezes with valuable insights into what different 'vibe coders' — tools aimed at assisting developers or enabling non-developers — prioritize. He observed that tools like Loveable, V0, and Bolt tend to focus heavily on enabling fast iteration, allowing users to quickly generate and modify code snippets or UI components. In contrast, tools like Manus, Devin, OpenAI Codex, and Replit are geared towards helping users create more complete, full-stack applications, although their output is often still raw code that requires further handling.

This comparative analysis helped Menezes identify a significant market opportunity. While existing tools focused on either fast iteration or raw code generation for developers, there was a gap in enabling non-programmers to build complete, functional applications, especially within the complex environment of an enterprise. This requires not just generating code, but also handling crucial aspects like security, access control, and seamless integration with existing enterprise data sources such as Salesforce, SAP, or internal databases.

This realization directly informed Superblocks' product strategy and the development of their Clark AI agent. By understanding the limitations and focus areas of existing AI coding tools through their prompts and inferred infrastructure, Superblocks positioned itself to address the unmet need for an enterprise-grade AI agent capable of securely generating and deploying internal applications, accessible even to users without traditional coding expertise.

While Superblocks may not yet be a multi-billion dollar 'unicorn,' this strategic approach has clearly resonated with the market. The company has successfully landed notable enterprise customers, including Instacart and Paypaya Global, demonstrating the value of their approach.

Dogfooding the AI Agent: Internal Validation

A strong testament to Superblocks' belief in their AI agent approach is their commitment to 'dogfooding' the product internally. Menezes shared that their software engineers are specifically tasked with building the core Superblocks product and are not permitted to write internal tools. Instead, the company's business teams — those without traditional programming backgrounds — are empowered to build the AI agents they need using the Superblocks platform.

This has led to the creation of various internal AI agents tailored to specific business needs. Examples include an agent that leverages CRM data to identify high-potential sales leads, another that tracks and analyzes support metrics to improve customer service, and an agent designed to balance the workload assignments among the human sales engineers. This internal adoption validates the platform's ability to enable non-technical users to create powerful, functional tools, proving the concept that effective AI agents, supported by robust infrastructure, can democratize application development within the enterprise.

"This is basically a way for us to build the tools and not buy the tools," Menezes stated, highlighting the efficiency and cost savings gained by enabling internal teams to create custom solutions using their own AI platform.

The Broader Implications for AI Development and Startups

The practice of studying system prompts extends beyond the realm of coding AI and holds significant implications for the broader AI development landscape and the search for new startup opportunities. As AI models become more capable and versatile, the way we interact with them — through prompts and the surrounding systems — becomes increasingly sophisticated.

Analyzing how successful AI products structure their prompts and build their infrastructure can provide insights into:

**Effective Communication with LLMs:** Learning the specific language, structure, and level of detail required to elicit reliable and desired behavior from foundational models.
**Designing Agentic Behavior:** Understanding how to break down complex tasks into steps, define tool use, and manage state to enable AI agents to perform multi-step operations.
**Building Robust AI Applications:** Recognizing the importance of the 80% infrastructure — data handling, security, monitoring, and integration — that turns a clever prompt into a production-ready product.
**Identifying Unmet Needs:** Spotting patterns in what existing AI tools *can* and *cannot* do effectively, based on their prompt structures and capabilities, thereby revealing opportunities for new, specialized AI products or platforms.
**Competitive Analysis:** Gaining a deeper understanding of competitors' core strategies and technical approaches by examining how they instruct their underlying AI models.

This approach suggests a shift in how we think about innovation in the AI era. While novel foundational models or groundbreaking algorithms are significant, much of the value and differentiation in application-level AI lies in the intelligent design of the interaction layer (the prompt) and the robust engineering of the supporting infrastructure. The 'secret sauce' is less about the core AI model itself (which is often a commodity from major providers) and more about the expertise in applying and augmenting it for specific use cases.

For aspiring entrepreneurs, this presents a tangible strategy for identifying startup ideas. Instead of solely focusing on entirely new problems, they can look at existing, widely used AI tools and ask:

How are these tools instructing the underlying models?
What specific roles, contexts, and tools are they leveraging?
What are the apparent limitations or areas of friction based on their prompt structure?
What infrastructure would be needed to make this type of AI capability more reliable, secure, or integrated for a specific vertical or user group?
Could a different combination of role, context, and tools unlock a new application for the same foundational model?

By systematically analyzing the operational mechanics of successful AI products through the lens of their system prompts and supporting infrastructure, entrepreneurs can uncover nuanced opportunities that might be missed by a more superficial market analysis. This requires a blend of technical understanding (of LLMs and prompt engineering) and market insight (to identify underserved needs).

Challenges and Considerations

While studying system prompts offers a valuable learning opportunity, it's not without its challenges. System prompts can be complex, lengthy, and sometimes obfuscated. Inferring the full 80% of the 'secret sauce' — the infrastructure — requires technical expertise and often involves making educated guesses based on the product's observed behavior.

Furthermore, relying too heavily on studying others' prompts could lead to derivative ideas rather than truly innovative ones. The goal is not to copy, but to learn the principles of effective AI application design and identify areas where existing approaches can be improved upon or applied to new domains.

There are also ethical considerations. While many prompts are accessible, some companies may consider them proprietary intellectual property. Accessing prompts through unintended means or attempting to replicate them too closely could raise legal or ethical issues. The focus should be on understanding the *techniques* and *strategies* embedded in the prompts, not on verbatim reproduction.

Finally, the AI landscape is constantly evolving. What constitutes an effective prompt or necessary infrastructure today may change as models improve and new techniques emerge. Continuous learning and adaptation are crucial.

The Future of Prompt Engineering and AI Agents

The emphasis on system prompts and prompt enrichment highlights the growing importance of prompt engineering as a discipline. It's no longer just about crafting a single query, but about designing a complex set of instructions and building the technical framework that allows AI models to perform sophisticated tasks reliably and safely.

As AI agents become more autonomous and capable of interacting with the real world, the design of their underlying instructions and the robustness of their supporting infrastructure will become even more critical. Studying how pioneers are building these agents today, by examining their prompts and systems, offers a glimpse into the future of AI development.

Companies like Superblocks are at the forefront of this trend, building platforms that abstract away some of the complexity of prompt engineering and infrastructure development, making it easier for more users to build powerful AI applications. Their success, fueled by significant investment and adoption by major enterprises, underscores the demand for tools that bridge the gap between foundational AI models and practical, domain-specific applications.

Brad Menezes' perspective offers a compelling framework for identifying the next wave of startup opportunities in AI. By treating the system prompts of successful AI products as valuable case studies in applied prompt engineering and infrastructure design, entrepreneurs can gain unique insights into what works, what's missing, and where the true potential for building billion-dollar companies lies. The secret to the next unicorn might just be hidden in plain sight, waiting to be discovered by those willing to study the instructions that guide today's AI giants.

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