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Interactive prompt engineering sandbox exploring Large Language Models (LLMs), attention mechanisms, and tokens.
Formulate structured AI prompts using Zero-shot, Few-shot, and Chain-of-Thought reasoning architectures.
A deep learning architecture that processes text sequences using self-attention mechanisms to weigh token context.
The sub-word chunk processed by language models, roughly averaging 4 characters in length.
Instructing an AI model to articulate intermediate reasoning steps before delivering final outputs.
Recommended Educational Guide
Internet Architecture & AI Servers
Use these established frameworks to write requests with maximum scientific precision.
Forces the AI to break down its logical thinking using clauses like: "Piensa paso a paso y justifica cada premisa antes de dictaminar la conclusión final". This mitigates logical hallucinations by 40% in mathematical and software analysis.
Provides concrete examples of the input-output relationship (Input/Output). Giving the AI 2-4 worked examples of the exact expected format raises the structural consistency of the final result by more than 50%.
Special for analytical and code tasks. Forces the model to write under the hierarchy: Pensamiento -> Acción -> Observación. Helps solve problems where iterative contrasting of data is required.
Designed by the Singapore government, **CO-STAR** is the gold standard for structuring any directive instruction:
Context: Provides the background setting or initial situation.
Objective: Defines the exact task that the model must execute.
Style: Indicates the tone or brand guidelines (e.g., persuasive, concise, academic).
Tone: Set the attitude of the response (e.g. professional, informal, empathetic).
Audience: Who is it aimed at (e.g. software experts, B2B investors, 10-year-old children).
Response: Specifies the delivery format (e.g. markdown table, JSON, ordered list).