Introduction: this article rewrites and translates into Spanish a practical guide for directing language models in the generation of code and projects, maintaining the idea of improving productivity and the quality of the result without overusing the term vibe coding.
1) The plan: create a PLAN.md file that summarizes the architecture and rules of the project concisely. Include the main objective, which in this example is to build an in-memory product store that can be searched by keyword using the Echo framework. Define the necessary endpoints such as api slash v1 slash products to list, api slash v1 slash products with POST method to validate and store, api slash v1 slash products slash id with GET method to get by id, and api slash v1 slash products slash upsert with POST method to insert or update depending on whether the id exists. Establish a standard response format with two top-level fields called messages and data, where messages is a list of messages and data contains the payload, whether it is a product, a list, or pagination metadata such as page, perPage, totalPages, and totalRecords. Indicate code style: modularity, single-responsibility functions, no comments in the code, and use of a concurrency-safe structure such as sync.Map to avoid race conditions. Propose a simple file structure with main.go, a controllers folder with add_products.go and list_products.go, and models with types.go for the structures and store.go for in-memory storage.
2) Step-by-step direction: ask the model to read PLAN.md and build a mental map of the rules. Task 1: create the folder structure, the product struct in types.go, and a main.go with a basic Echo main function. The product struct should be based on general e-commerce knowledge and include common fields such as id, name, description, price, brand, category, stock, attributes, and creation date. Do not create endpoints or controllers yet. Task 2: in types.go, create a Response structure that always returns the two top-level fields messages and data according to the defined format. Create a BaseController with a RenderResponse method that receives the Response and an HTTP code and generates a JSON response compatible with Echo. All future controllers will extend BaseController. Recommend reviewing and adjusting the code after each step and keeping clean commits to facilitate small diffs.
3) Specialized agents: suggest dividing the work among agents or roles with clear responsibilities, for example an agent that only handles style and design, another that generates unit tests, another that implements endpoints. Create style files or guides such as style.md, golang.md, and tests.md and limit each agent to specific tasks. Including screenshots or diagrams as reference can help. This division allows parallelizing tasks without overlapping files and facilitates specific reviews.
4) Use small models when possible: not all tasks require large models. For routine and replicable tasks such as generating configuration files, Dockerfiles, migrations, or simple CRUD, use small and fast models to save time and resources. Reserve higher-capacity models for complex planning, verification, and tasks that require deep reasoning.
5) Meta prompting: ask a powerful model to generate a set of small and specific prompts optimized for smaller models. Create a script that iterates through those prompts and executes the tasks concurrently when there are no file conflicts. Finally, use the powerful model to validate that the delivered work meets the plan's requirements.
Practical tips: keep prompts clear, objective, and concise. Document design decisions in small files that agents can use as reference. Avoid overloading the context with unnecessary information to improve the quality of generation.
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