Humans, Machines, and Ratatouille 🐀

Source: Dev.to

Introduction

A pragmatic response to system complexity in AI systems

For a long time, we designed digital products the way many people imagine a kitchen works: if the dish looks good, the job is done. We optimized for presentation—polished slides, beautiful PDFs, documents that look right. They became the default containers for organizational knowledge, and for humans, that mostly worked.

The Problem with Current Knowledge Presentation

When agents and large language models (LLMs) entered the “kitchen,” presentation was no longer enough. Today, the same information must be consumable not only by people but also by systems that search, reuse, summarize, and reason over it. Yet we keep handing machines beautifully plated dishes without recipes and then blame the kitchen when the result is inconsistent.

Many of the difficulties we attribute to AI complexity originate much earlier, in how knowledge is prepared.

Retrieval‑Augmented Generation (RAG) and Its Friction

In many organizations, the first serious attempt to apply AI to internal knowledge comes through Retrieval‑Augmented Generation (RAG). The idea sounds simple: take existing documents, connect them to a model, and ask questions.

In practice, friction appears. When knowledge lives in PDFs or slide decks, RAG systems must reconstruct structure that was never explicit. Headings are inferred, sections are guessed, and chunking becomes heuristic. From the system’s point of view, this is not following a recipe; it is tasting the dish and guessing the ingredients.

The symptoms are familiar:

• Noisy retrieval
• Inconsistent answers
• Layers of fixes that increase cost and fragility

At some point, the question becomes unavoidable: Why are we spending so much effort interpreting information that humans already understand?

Lessons from Ratatouille

Ratatouille captures this problem surprisingly well. Remy is an exceptional cook, and Linguini can function inside a professional kitchen. Individually, neither can consistently produce chef‑level dishes. What makes their collaboration work is not talent but a shared operational language—gestures, constraints, and conventions that keep intent and execution aligned. Without that language, the kitchen collapses into chaos; with it, results become repeatable.

Modern AI systems face the same challenge. Humans understand meaning intuitively, while machines execute with precision. Without a shared representation of knowledge, systems are forced to guess.

Why Structured Formats Matter

A professional kitchen does not rely on plating to function; it relies on recipes. Recipes make structure explicit and allow multiple cooks to coordinate without guessing. Designing for humans and machines follows the same logic.

• Humans need narrative and readability.
• Machines need explicit structure and unambiguous meaning.

This is where text‑based, structured formats become critical.

Markdown as a Coordination Layer

Markdown works not because it is simple, but because it is explicit. It is readable by humans, easy to version and diff, and straightforward to process programmatically. More importantly, Markdown treats text as an interface. When agents and LLMs interact with knowledge, they do not consume visuals; they consume structure.

A single Markdown source can be rendered for humans while remaining directly consumable by systems, pipelines, and automated agents. In that sense, Markdown is less about documentation and more about coordination.

Implications for RAG Systems

Many limitations in RAG systems are not rooted in model capability but in how knowledge was structured in the first place. When rendered documents become the source of truth, downstream systems must reverse‑engineer meaning after the fact. When knowledge is created in structured, semantically transparent formats first, entire classes of problems disappear:

• Retrieval improves
• Maintenance drops
• Systems become easier to extend

In AI systems, representation is often the hidden bottleneck.

Conclusion

Ratatouille is not really a story about learning how to cook—Remy already had the skills. The real challenge was making collaboration possible inside a complex system.

Where in your systems are machines still guessing the recipe from the final dish? Sometimes, optimizing development starts with writing the recipe before plating the dish.