feat(pages): First batch of spanish pages

pages/_meta.es.json

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+{
+  "index": "Ingeniería de Prompt",
+  "introduction": "Introducción",
+  "techniques": "Técnicas",
+  "applications": "Aplicaciones",
+  "models": "Modelos",
+  "risks": "Riesgos y Malos Usos",
+  "papers": "Papers",
+  "tools": "Herramientas",
+  "notebooks": "Notebooks",
+  "datasets": "Datasets",
+  "readings": "Lecturas Adicionales",
+  "about": {
+    "title": "Acerca de",
+    "type": "page"
+  },
+  "course": {
+    "title": "Curso de Ingeniería de Prompt",
+    "type": "page"
+  },
+  "contact": {
+    "title": "Contactar ↗",
+    "type": "page",
+    "href": "https://twitter.com/dair_ai",
+    "newWindow": true
+  }
+}

pages/about.es.mdx

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+# Acerca de
+
+La Guía de Ingeniería de Prompt es un proyecto de [DAIR.AI](https://github.com/dair-ai). Su objetivo es educar a investigadores y profesionales sobre la ingeniería de prompts.
+
+DAIR.AI tiene como objetivo democratizar la investigación en inteligencia artificial, la educación y las tecnologías. Nuestra misión es permitir la próxima generación de innovadores y creadores de IA.
+
+Damos la bienvenida a contribuciones de la comunidad. Busque los botones de Edición.
+
+Información de licencia [aquí](https://github.com/dair-ai/Prompt-Engineering-Guide#license).
+
+Tomamos inspiración de muchos recursos abiertos como [OpenAI CookBook](https://github.com/openai/openai-cookbook), [Pretrain, Prompt, Predict](http://pretrain.nlpedia.ai/), [Learn Prompting](https://learnprompting.org/) y muchos otros.

pages/applications.es.mdx

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+# Aplicaciones del Prompting
+
+import { Callout } from 'nextra-theme-docs'
+
+In this section, we will cover some advanced and interesting ways we can use prompt engineering to perform useful and more advanced tasks.
+
+En esta sección se mostrarán algunas formas avanzadas e interesantes en las que podemos usar la ingenieria de prompts para realizar tareas más avanzadas y útiles.
+
+<Callout emoji="⚠️">
+Esta sección está en pleno desarrollo.
+</Callout>

pages/course.es.mdx

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+# Curso de Ingeniería de Prompt
+
+Nos hemos asociado con Sphere para ofrecer un curso de ["Ingeniería de Prompt para LLMs"](https://www.getsphere.com/cohorts/prompt-engineering-for-llms?source=promptingguide) en mayo de 2023.
+
+Este curso práctico está diseñado para enseñar todas las últimas técnicas y herramientas de ingeniería de prompt utilizadas en el mundo real para construir aplicaciones de manera efectiva sobre grandes modelos de lenguaje.
+
+Si deseas llevar tus habilidades de ingeniería de prompt al siguiente nivel, recomendamos encarecidamente el curso.
+
+Este curso también incluye un certificado de finalización.

pages/datasets.es.mdx

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+# Datasets
+
+#### (Ordenados por nombre)
+
+- [Anthropic's Red Team dataset](https://github.com/anthropics/hh-rlhf/tree/master/red-team-attempts), [(paper)](https://arxiv.org/abs/2209.07858)
+- [Awesome ChatGPT Prompts](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts)
+- [DiffusionDB](https://github.com/poloclub/diffusiondb)
+- [Midjourney Prompts](https://huggingface.co/datasets/succinctly/midjourney-prompts)
+- [P3 - Public Pool of Prompts](https://huggingface.co/datasets/bigscience/P3)
+- [PartiPrompts](https://parti.research.google)
+- [Real Toxicity Prompts](https://allenai.org/data/real-toxicity-prompts)
+- [Stable Diffusion Dataset](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts)
+- [WritingPrompts](https://www.reddit.com/r/WritingPrompts)

pages/index.es.mdx

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+# Guía de Ingeniería de Prompt
+
+La ingeniería de prompt es una disciplina relativamente nueva para el desarrollo y la optimización de prompts para utilizar eficientemente modelos de lenguaje (ML) en una amplia variedad de aplicaciones y temas de investigación. Las habilidades de ingeniería de prompt ayudan a comprender mejor las capacidades y limitaciones de los grandes modelos de lenguaje (LLM).
+
+Los investigadores utilizan la ingeniería de prompt para mejorar la capacidad de los LLM en una amplia gama de tareas comunes y complejas, como responder preguntas y razonamiento aritmético. Los desarrolladores utilizan la ingeniería de prompt para diseñar técnicas de prompt robustas y efectivas que interactúen con los LLM y otras herramientas.
+
+La ingeniería de prompt no solo se trata de diseñar y desarrollar prompts. Abarca una amplia gama de habilidades y técnicas útiles para interactuar y desarrollar con LLM. Es una habilidad importante para interactuar, construir y comprender las capacidades de los LLM. Puedes utilizar la ingeniería de prompt para mejorar la seguridad de los LLM y crear nuevas capacidades, como la incorporación de conocimientos de dominio y herramientas externas a los LLM.
+
+Motivados por el gran interés en el desarrollo con LLM, hemos creado esta nueva guía de ingeniería de prompt que contiene todos los últimos artículos, guías de aprendizaje, modelos, conferencias, referencias, nuevas capacidades de LLM y herramientas relacionadas con la ingeniería de prompt.

pages/introduction.es.mdx

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+# Introducción
+
+La ingeniería de prompt es una disciplina relativamente nueva para el desarrollo y la optimización de prompts para utilizar eficientemente modelos de lenguaje (ML) en una amplia variedad de aplicaciones y temas de investigación. Las habilidades de ingeniería de prompt ayudan a comprender mejor las capacidades y limitaciones de los grandes modelos de lenguaje (LLM). Los investigadores utilizan la ingeniería de prompt para mejorar la capacidad de los LLM en una amplia gama de tareas comunes y complejas, como responder preguntas y razonamiento aritmético. Los desarrolladores utilizan la ingeniería de prompt para diseñar técnicas de prompt robustas y efectivas que interactúen con los LLM y otras herramientas.
+
+Esta guía cubre los conceptos básicos de los prompts para proporcionar una idea general de cómo utilizar los prompts para interactuar e instruir a los grandes modelos de lenguaje (LLM).
+
+Todos los ejemplos se han probado con `text-davinci-003` (usando el playground de OpenAI) a menos que se especifique lo contrario. Se utilizan las configuraciones predeterminadas, es decir, `temperature=0.7` y `top-p=1`.

pages/models.es.mdx

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+# Modelos
+
+import { Callout } from 'nextra-theme-docs'
+
+En esta sección, cubriremos algunos de los modelos de lenguaje más recientes y cómo aplican con éxito las últimas y más avanzadas técnicas de ingeniería de generación de texto. Además, abarcamos las capacidades de estos modelos en una variedad de tareas y configuraciones de generación de texto, como la generación de texto con pocos ejemplos, la generación de texto sin ejemplos y la generación de texto de encadenamiento de pensamiento. Comprender estas capacidades es importante para entender las limitaciones de estos modelos y cómo utilizarlos de manera efectiva.
+
+<Callout emoji="⚠️">
+Esta sección está en pleno desarrollo.
+</Callout>

pages/notebooks.es.mdx

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+# Prompt Engineering Notebooks
+
+Contains a collection of noteooks we have designed to help you get started with prompt engineering. More to be added soon!
+
+| Description   | Notebook   | 
+| :------------ | :---------: | 
+|Learn how to perform many different types of common tasks using the `openai` and `LangChain` library|[Getting Started with Prompt Engineering](https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/pe-lecture.ipynb)|
+|Learn how to use code as reasoning for solving common tasks using the Python interpreter in combination with the language model.|[Program-Aided Language Model](https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/pe-pal.ipynb)|
+|Learn more about how to make calls to the ChatGPT APIs using the `openai` library.|[ChatGPT API Intro](https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/pe-chatgpt-intro.ipynb)|
+|Learn how to use ChatGPT features using the `LangChain` library. |[ChatGPT API with LangChain](https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/pe-chatgpt-langchain.ipynb)|
+|Learn about adversarial prompting include defensive measures.|[Adversarial Prompt Engineering](https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/pe-chatgpt-adversarial.ipynb)|

pages/papers.es.mdx

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+# Papers
+
+A continuación se presentan los últimos artículos (ordenados por fecha de publicación) sobre ingeniería de prompts. Actualizamos esto diariamente y siempre hay nuevos artículos disponibles. Incorporamos resúmenes de estos artículos en las guías mencionadas anteriormente cada semana.
+
+## Descripciones generales
+
+  - [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://arxiv.org/abs/2304.02020) (April 2023)
+  - [A Survey of Large Language Models](https://arxiv.org/abs/2303.18223) (April 2023)
+  - [Nature Language Reasoning, A Survey](https://arxiv.org/abs/2303.14725) (Mar 2023)
+  - [Augmented Language Models: a Survey](https://arxiv.org/abs/2302.07842) (Feb 2023)
+  - [A Survey for In-context Learning](https://arxiv.org/abs/2301.00234) (Dec 2022)
+  - [Towards Reasoning in Large Language Models: A Survey](https://arxiv.org/abs/2212.10403) (Dec 2022)
+  - [Reasoning with Language Model Prompting: A Survey](https://arxiv.org/abs/2212.09597) (Dec 2022)
+  - [Emergent Abilities of Large Language Models](https://arxiv.org/abs/2206.07682) (Jun 2022)
+  - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (Apr 2022)
+  - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) (Jul 2021)
+
+## Enfoques
+
+  - [Why think step-by-step? Reasoning emerges from the locality of experience](https://arxiv.org/abs/2304.03843) (April 2023)
+  - [Revisiting Automated Prompting: Are We Actually Doing Better?](https://arxiv.org/abs/2304.03609) (April 2023)
+  - [REFINER: Reasoning Feedback on Intermediate Representations](https://arxiv.org/abs/2304.01904) (April 2023)
+  - [Reflexion: an autonomous agent with dynamic memory and self-reflection](https://arxiv.org/abs/2303.11366) (March 2023)
+  - [CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society](https://arxiv.org/abs/2303.17760) (Mar 2023)
+  - [Self-Refine: Iterative Refinement with Self-Feedback](https://arxiv.org/abs/2303.17651v1) (Mar 2023)
+  - [kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference](https://arxiv.org/abs/2303.13824) (Mar 2023)
+  - [Visual-Language Prompt Tuning with Knowledge-guided Context Optimization](https://arxiv.org/abs/2303.13283) (Mar 2023)
+  - [Fairness-guided Few-shot Prompting for Large Language Models](https://arxiv.org/abs/2303.13217) (Mar 2023)
+  - [Context-faithful Prompting for Large Language Models](https://arxiv.org/abs/2303.11315) (Mar 2023)
+  - [Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning](https://arxiv.org/abs/2303.10475) (Mar 2023)
+  - [UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation](https://arxiv.org/abs/2303.08518) (Mar 2023)
+  - [Model-tuning Via Prompts Makes NLP Models Adversarially Robust](https://arxiv.org/abs/2303.07320) (Mar 2023)
+  - [Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer](https://arxiv.org/abs/2303.03922) (March 2023)
+  - [CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification](https://arxiv.org/abs/2303.03628) (March 2023)
+  - [Larger language models do in-context learning differently](https://arxiv.org/abs/2303.03846) (March 2023)
+  - [OpenICL: An Open-Source Framework for In-context Learning](https://arxiv.org/abs/2303.02913) (March 2023)
+  - [Dynamic Prompting: A Unified Framework for Prompt Tuning](https://arxiv.org/abs/2303.02909) (March 2023)
+  - [Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning](https://arxiv.org/abs/2303.02861) (March 2023)
+  - [Effectiveness of Data Augmentation for Prefix Tuning with Limited Data](https://arxiv.org/abs/2303.02577) (March 2023)
+  - [Mixture of Soft Prompts for Controllable Data Generation](https://arxiv.org/abs/2303.01580) (March 2023)
+  - [Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners](https://arxiv.org/abs/2303.02151) (March 2023)
+  - [How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks](https://arxiv.org/abs/2303.00293) (March 2023)
+  - [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://arxiv.org/pdf/2302.10198.pdf) (Feb 2023)
+  - [EvoPrompting: Language Models for Code-Level Neural Architecture Search](https://arxiv.org/abs/2302.14838) (Feb 2023)
+  - [In-Context Instruction Learning](https://arxiv.org/abs/2302.14691) (Feb 2023)
+  - [Chain of Hindsight Aligns Language Models with Feedback](https://arxiv.org/abs/2302.02676) (Feb 2023)
+  - [Language Is Not All You Need: Aligning Perception with Language Models](https://arxiv.org/abs/2302.14045) (Feb 2023)
+  - [Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data](https://arxiv.org/abs/2302.12822) (Feb 2023)
+  - [Active Prompting with Chain-of-Thought for Large Language Models](https://arxiv.org/abs/2302.12246) (Feb 2023)
+  - [More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models](https://arxiv.org/abs/2302.12173) (Feb 2023)
+  - [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://arxiv.org/abs/2302.11382) (Feb 2023)
+  - [Guiding Large Language Models via Directional Stimulus Prompting](https://arxiv.org/abs/2302.11520) (Feb 2023)
+  - [How Does In-Context Learning Help Prompt Tuning?](https://arxiv.org/abs/2302.11521) (Feb 2023)
+  - [Scalable Prompt Generation for Semi-supervised Learning with Language Models](https://arxiv.org/abs/2302.09236) (Feb 2023)
+  - [Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints](https://arxiv.org/abs/2302.09185) (Feb 2023)
+  - [À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting](https://arxiv.org/abs/2302.07994) (Feb 2023)
+  - [GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://arxiv.org/abs/2302.08043) (Feb 2023)
+  - [The Capacity for Moral Self-Correction in Large Language Models](https://arxiv.org/abs/2302.07459) (Feb 2023)
+  - [SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains](https://arxiv.org/abs/2302.06868) (Feb 2023)
+  - [Evaluating the Robustness of Discrete Prompts](https://arxiv.org/abs/2302.05619) (Feb 2023)
+  - [Compositional Exemplars for In-context Learning](https://arxiv.org/abs/2302.05698) (Feb 2023)
+  - [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://arxiv.org/abs/2302.03668) (Feb 2023)
+  - [Multimodal Chain-of-Thought Reasoning in Language Models](https://arxiv.org/abs/2302.00923) (Feb 2023)
+  - [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://arxiv.org/abs/2302.00093) (Feb 2023)
+  - [Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models](https://arxiv.org/abs/2302.00618) (Feb 2023)
+  - [Progressive Prompts: Continual Learning for Language Models](https://arxiv.org/abs/2301.12314) (Jan 2023)
+  - [Batch Prompting: Efficient Inference with LLM APIs](https://arxiv.org/abs/2301.08721) (Jan 2023)
+  - [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://arxiv.org/abs/2212.14024) (Dec 2022)
+  - [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://arxiv.org/abs/2212.08061) (Dec 2022)
+  - [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073) (Dec 2022)
+  - [Successive Prompting for Decomposing Complex Questions](https://arxiv.org/abs/2212.04092) (Dec 2022)
+  - [Large Language Models are reasoners with Self-Verification](https://arxiv.org/abs/2212.09561v1) (Dec 2022)
+  - [Discovering Language Model Behaviors with Model-Written Evaluations](https://arxiv.org/abs/2212.09251) (Dec 2022)
+  - [Structured Prompting: Scaling In-Context Learning to 1,000 Examples](https://arxiv.org/abs/2212.06713) (Dec 2022)
+  - [PAL: Program-aided Language Models](https://arxiv.org/abs/2211.10435) (Nov 2022)
+  - [Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910) (Nov 2022)
+  - [Ignore Previous Prompt: Attack Techniques For Language Models](https://arxiv.org/abs/2211.09527) (Nov 2022)
+  - [Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods](https://arxiv.org/abs/2210.07321) (Nov 2022)
+  - [Teaching Algorithmic Reasoning via In-context Learning](https://arxiv.org/abs/2211.09066) (Nov 2022)
+  - [Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference](https://arxiv.org/abs/2211.11875) (Nov 2022)
+  - [Ask Me Anything: A simple strategy for prompting language models](https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for) (Oct 2022)
+  - [Recitation-Augmented Language Models](https://arxiv.org/abs/2210.01296) (Oct 2022)
+  - [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) (Oct 2022)
+  - [Prompting GPT-3 To Be Reliable](https://arxiv.org/abs/2210.09150) (Oct 2022)
+  - [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://arxiv.org/abs/2210.02406) (Oct 2022)
+  - [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://arxiv.org/abs/2210.01240v3) (Oct 2022)
+  - [Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples](https://arxiv.org/abs/2209.02128) (Sep 2022)
+  - [Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning](https://arxiv.org/abs/2209.14610) (Sep 2022)
+  - [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://arxiv.org/abs/2209.11755) (Sep 2022)
+  - [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://arxiv.org/abs/2208.03299) (Nov 2022)
+  - [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/abs/2207.05987) (July 2022)
+  - [On the Advance of Making Language Models Better Reasoners](https://arxiv.org/abs/2206.02336) (June 2022)
+  - [Large Language Models are Zero-Shot Reasoners](https://arxiv.org/abs/2205.11916) (May 2022)
+  - [Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations](https://arxiv.org/abs/2205.11822) (May 2022)
+  - [MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning](https://arxiv.org/abs/2205.00445) (May 2022)
+  - [PPT: Pre-trained Prompt Tuning for Few-shot Learning](https://aclanthology.org/2022.acl-long.576/) (Mqy 2022)
+  - [Toxicity Detection with Generative Prompt-based Inference](https://arxiv.org/abs/2205.12390) (May 2022)
+  - [Learning to Transfer Prompts for Text Generation](https://arxiv.org/abs/2205.01543) (May 2022)
+  - [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://arxiv.org/abs/2205.03401) (May 2022)
+  - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (Apr 2022)
+  - [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://arxiv.org/abs/2203.06566) (Mar 2022)
+  - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) (March 2022)
+  - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
+  - [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://arxiv.org/abs/2202.12837) (Feb 2022)
+  - [Chain of Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) (Jan 2022)
+  - [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://arxiv.org/abs/2112.00114) (Nov 2021)
+  - [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://arxiv.org/abs/2110.01691) (Oct 2021)
+  - [Generated Knowledge Prompting for Commonsense Reasoning](https://arxiv.org/abs/2110.08387) (Oct 2021)
+  - [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) (Oct 2021)
+  - [Reframing Instructional Prompts to GPTk's Language](https://arxiv.org/abs/2109.07830) (Sep 2021)
+  - [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://arxiv.org/abs/2109.06977) (Sep 2021)
+  - [Making Pre-trained Language Models Better Few-shot Learners](https://aclanthology.org/2021.acl-long.295) (Aug 2021)
+  - [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://arxiv.org/abs/2104.08786) (April 2021)
+  - [BERTese: Learning to Speak to BERT](https://aclanthology.org/2021.eacl-main.316) (April 2021)
+  - [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) (April 2021)
+  - [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://arxiv.org/abs/2102.07350) (Feb 2021)
+  - [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://arxiv.org/abs/2102.09690) (Feb 2021)
+  - [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/abs/2101.00190) (Jan 2021)
+  - [Learning to Generate Task-Specific Adapters from Task Description](https://arxiv.org/abs/2101.00420) (Jan 2021)
+  - [Making Pre-trained Language Models Better Few-shot Learners](https://arxiv.org/abs/2012.15723) (Dec 2020)
+  - [Learning from Task Descriptions](https://aclanthology.org/2020.emnlp-main.105/) (Nov 2020)
+  - [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://arxiv.org/abs/2010.15980) (Oct 2020)
+  - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) (May 2020)
+  - [How Can We Know What Language Models Know?](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00324/96460/How-Can-We-Know-What-Language-Models-Know) (July 2020)
+  - [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) (Jan 2020)
+
+## Aplicaciones
+
+  - [Automated Reading Passage Generation with OpenAI's Large Language Model](https://arxiv.org/abs/2304.04616) (April 2023)
+  - [WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus](https://arxiv.org/abs/2304.04358) (April 2023)
+  - [Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition](https://arxiv.org/abs/2304.04704) (April 2023)
+  - [GPT detectors are biased against non-native English writers](https://arxiv.org/abs/2304.02819) (April 2023)
+  - [Zero-Shot Next-Item Recommendation using Large Pretrained Language Models](https://arxiv.org/abs/2304.03153) (April 2023)
+  - [Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT](https://arxiv.org/abs/2304.02213) (April 2023)
+  - [Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning](https://arxiv.org/abs/2304.01295) (April 2023)
+  - [Better Language Models of Code through Self-Improvement](https://arxiv.org/abs/2304.01228) (April 2023)
+  - [PromptORE -- A Novel Approach Towards Fully Unsupervised Relation Extraction](https://arxiv.org/abs/2304.01209) (April)
+  - [Assessing Language Model Deployment with Risk Cards]() (April 2023)
+  - [Enhancing Large Language Models with Climate Resources](https://arxiv.org/abs/2304.00116) (March 2023)
+  - [BloombergGPT: A Large Language Model for Finance](https://arxiv.org/abs/2303.17564) (March 2023)
+  - [Medical Intervention Duration Estimation Using Language-enhanced Transformer Encoder with Medical Prompts](https://arxiv.org/abs/2303.17408) (March 2023)
+  - [Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes](https://arxiv.org/abs/2303.15846) (March 2023)
+  - [TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs](https://arxiv.org/abs/2303.16434) (March 2023)
+  - [Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning](https://arxiv.org/abs/2303.16445) (March 2023)
+  - [Linguistically Informed ChatGPT Prompts to Enhance Japanese-Chinese Machine Translation: A Case Study on Attributive Clauses](https://arxiv.org/abs/2303.15587) (March 2023)
+  - [Knowledge-augmented Frame Semantic Parsing with Hybrid Prompt-tuning](https://arxiv.org/abs/2303.14375) (March 2023)
+  - [Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation](https://arxiv.org/abs/2303.15413) (March 2023)
+  - [Zero-shot Model Diagnosis](https://arxiv.org/abs/2303.15441#) (March 2023)
+  - [Prompting Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages](https://arxiv.org/abs/2303.13592) (March 2023)
+  - [SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization](https://arxiv.org/abs/2303.13035) (March 2023)
+  - [Large Language Models and Simple, Stupid Bugs](https://arxiv.org/abs/2303.11455) (March 2023)
+  - [Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?](https://arxiv.org/abs/2303.09325) (Mar 2023)
+  - [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://arxiv.org/abs/2303.08896) (Mar 2023)
+  - [Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification](https://arxiv.org/abs/2303.07142) (March 2023)
+  - [ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction](https://arxiv.org/abs/2303.05063) (March 2023)
+  - [MathPrompter: Mathematical Reasoning using Large Language Models](https://arxiv.org/abs/2303.05398) (March 2023)
+  - [Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums](https://arxiv.org/abs/2303.05400) (March 2023)
+  - [Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting](https://arxiv.org/abs/2303.03199) (March 2023)
+  - [Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering](https://arxiv.org/abs/2303.01903) (March 2023)
+  - [Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis](https://arxiv.org/abs/2303.00815) (March 2023)
+  - [SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks](https://arxiv.org/abs/2303.00733) (March 2023)
+  - [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://arxiv.org/abs/2302.14233) (Feb 2023)
+  - [Navigating the Grey Area: Expressions of Overconfidence and Uncertainty in Language Models](https://arxiv.org/abs/2302.13439) (Feb 2023)
+  - [TabGenie: A Toolkit for Table-to-Text Generation](https://arxiv.org/abs/2302.14169) (Feb 2023)
+  - [SGL-PT: A Strong Graph Learner with Graph Prompt Tuning](https://arxiv.org/abs/2302.12449) (Feb 2023)
+  - [Few-Shot Table-to-Text Generation with Prompt-based Adapter](https://arxiv.org/abs/2302.12468) (Feb 2023)
+  - [Language Models Are Few-shot Learners for Prognostic Prediction](https://arxiv.org/abs/2302.12692) (Feb 2023)
+  - [STA: Self-controlled Text Augmentation for Improving Text Classifications](https://arxiv.org/abs/2302.12784) (Feb 2023)
+  - [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://arxiv.org/abs/2302.12813) (Feb 2023)
+  - [How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study](https://arxiv.org/abs/2302.10916) (Feb 2023)
+  - [Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales](https://arxiv.org/abs/2302.08961) (Feb 2023)
+  - [LabelPrompt: Effective Prompt-based Learning for Relation Classification](https://arxiv.org/abs/2302.08068) (Feb 2023)
+  - [Language Model Crossover: Variation through Few-Shot Prompting](https://arxiv.org/abs/2302.09236) (Feb 2023)
+  - [Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition](https://arxiv.org/abs/2302.08102) (Feb 2023)
+  - [The Capacity for Moral Self-Correction in Large Language Models](https://arxiv.org/abs/2302.07459) (Feb 2023)
+  - [Prompting for Multimodal Hateful Meme Classification](https://arxiv.org/abs/2302.04156) (Feb 2023)
+  - [PLACES: Prompting Language Models for Social Conversation Synthesis](https://arxiv.org/abs/2302.03269) (Feb 2023)
+  - [Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation](https://arxiv.org/abs/2302.01441) (Feb 2023)
+  - [Crawling the Internal Knowledge-Base of Language Models](https://arxiv.org/abs/2301.12810) (Jan 2023)
+  - [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199) (Dec 2022)
+  - [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462) (Nov 2022)
+  - [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://arxiv.org/abs/2209.09513v2) (Sep 2022)
+  - [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://arxiv.org/abs/2210.15157) (Oct 2022)
+  - [Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?](https://arxiv.org/abs/2210.14699) (Oct 2022)
+  - [Plot Writing From Scratch Pre-Trained Language Models](https://aclanthology.org/2022.inlg-main.5) (July 2022)
+  - [Survey of Hallucination in Natural Language Generation](https://arxiv.org/abs/2202.03629) (Feb 2022)
+
+## Colecciones
+
+  - [Chain-of-Thought Papers](https://github.com/Timothyxxx/Chain-of-ThoughtsPapers)
+  - [Papers with Code](https://paperswithcode.com/task/prompt-engineering)
+  - [Prompt Papers](https://github.com/thunlp/PromptPapers#papers)

pages/readings.es.mdx

@@ -0,0 +1,118 @@
+# Lecturas adicionales
+#### (Ordenadas por nombre)
+
+- [2023 AI Index Report](https://aiindex.stanford.edu/report/)
+- [3 Principles for prompt engineering with GPT-3](https://www.linkedin.com/pulse/3-principles-prompt-engineering-gpt-3-ben-whately)
+- [Eight Things to Know about Large Language Models](https://arxiv.org/pdf/2304.00612v1.pdf)
+- [A beginner-friendly guide to generative language models - LaMBDA guide](https://aitestkitchen.withgoogle.com/how-lamda-works)
+- [A Complete Introduction to Prompt Engineering for Large Language Models](https://www.mihaileric.com/posts/a-complete-introduction-to-prompt-engineering)
+- [A Generic Framework for ChatGPT Prompt Engineering](https://medium.com/@thorbjoern.heise/a-generic-framework-for-chatgpt-prompt-engineering-7097f6513a0b)
+- [An SEO’s guide to ChatGPT prompts](https://searchengineland.com/chatgpt-prompts-seo-393523)
+- [Anyone can Design! With a little help from Generative AI](https://github.com/YashSharma/PromptEngineering)
+- [AI Content Generation](https://www.jonstokes.com/p/ai-content-generation-part-1-machine)
+- [AI's rise generates new job title: Prompt engineer](https://www.axios.com/2023/02/22/chatgpt-prompt-engineers-ai-job)
+- [AI Safety, RLHF, and Self-Supervision - Jared Kaplan | Stanford MLSys #79](https://www.youtube.com/watch?v=fqC3D-zNJUM&ab_channel=StanfordMLSysSeminars)
+- [Awesome Textual Instruction Learning Papers](https://github.com/RenzeLou/awesome-instruction-learning)
+- [Awesome ChatGPT Prompts](https://github.com/f/awesome-chatgpt-prompts)
+- [Best 100+ Stable Diffusion Prompts](https://mpost.io/best-100-stable-diffusion-prompts-the-most-beautiful-ai-text-to-image-prompts)
+- [Best practices for prompt engineering with OpenAI API](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api)
+- [Building GPT-3 applications — beyond the prompt](https://medium.com/data-science-at-microsoft/building-gpt-3-applications-beyond-the-prompt-504140835560)
+- [Can AI really be protected from text-based attacks?](https://techcrunch.com/2023/02/24/can-language-models-really-be-protected-from-text-based-attacks/)
+- [ChatGPT, AI and GPT-3 Apps and use cases](https://gpt3demo.com)
+- [ChatGPT Prompts](https://twitter.com/aaditsh/status/1636398208648658945?s=20)
+- [ChatGPT Plugins Collection ⭐️ (unofficial)](https://github.com/logankilpatrick/ChatGPT-Plugins-Collection)
+- [ChatGPT3 Prompt Engineering](https://github.com/mattnigh/ChatGPT3-Free-Prompt-List)
+- [CMU Advanced NLP 2022: Prompting](https://youtube.com/watch?v=5ef83Wljm-M&feature=shares)
+- [Common Sense as Dark Matter - Yejin Choi | Stanford MLSys #78](https://youtube.com/live/n4HakBqoCVg?feature=shares)
+- [Create images with your words – Bing Image Creator comes to the new Bing](https://blogs.microsoft.com/blog/2023/03/21/create-images-with-your-words-bing-image-creator-comes-to-the-new-bing/)
+- [Curtis64's set of prompt gists](https://gist.github.com/Curtis-64)
+- [CS324 - Large Language Models](https://stanford-cs324.github.io/winter2022/)
+- [CS 324 - Advances in Foundation Models](https://stanford-cs324.github.io/winter2023/)
+- [CS224N: Natural Language Processing with Deep Learning](https://web.stanford.edu/class/cs224n/)
+- [DALL·E 2 Prompt Engineering Guide](https://docs.google.com/document/d/11WlzjBT0xRpQhP9tFMtxzd0q6ANIdHPUBkMV-YB043U/edit#)
+- [DALL·E 2 Preview - Risks and Limitations](https://github.com/openai/dalle-2-preview/blob/main/system-card.md)
+- [DALLE Prompt Book](https://dallery.gallery/the-dalle-2-prompt-book)
+- [DALL-E, Make Me Another Picasso, Please](https://www.newyorker.com/magazine/2022/07/11/dall-e-make-me-another-picasso-please?)
+- [Diffusion Models: A Practical Guide](https://scale.com/guides/diffusion-models-guide)
+- [Exploiting GPT-3 Prompts](https://twitter.com/goodside/status/1569128808308957185)
+- [Exploring Prompt Injection Attacks](https://research.nccgroup.com/2022/12/05/exploring-prompt-injection-attacks)
+- [Extrapolating to Unnatural Language Processing with GPT-3's In-context Learning: The Good, the Bad, and the Mysterious](http://ai.stanford.edu/blog/in-context-learning)
+- [FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering](https://arxiv.org/pdf/2303.10699.pdf)
+- [Generative AI with Cohere: Part 1 - Model Prompting](https://txt.cohere.ai/generative-ai-part-1)
+- [Generative AI: Perspectives from Stanford HAI](https://hai.stanford.edu/sites/default/files/2023-03/Generative_AI_HAI_Perspectives.pdf)
+- [Get a Load of This New Job: "Prompt Engineers" Who Act as Psychologists to AI Chatbots](https://futurism.com/prompt-engineers-ai)
+- [Giving GPT-3 a Turing Test](https://lacker.io/ai/2020/07/06/giving-gpt-3-a-turing-test.html)
+- [GPT-3 & Beyond](https://youtube.com/watch?v=-lnHHWRCDGk)
+- [GPT3 and Prompts: A quick primer](https://buildspace.so/notes/intro-to-gpt3-prompts)
+- [Hands-on with Bing’s new ChatGPT-like features](https://techcrunch.com/2023/02/08/hands-on-with-the-new-bing/)
+- [How to Draw Anything](https://andys.page/posts/how-to-draw)
+- [How to get images that don't suck](https://www.reddit.com/r/StableDiffusion/comments/x41n87/how_to_get_images_that_dont_suck_a)
+- [How to make LLMs say true things](https://evanjconrad.com/posts/world-models)
+- [How to perfect your prompt writing for AI generators](https://www.sydney.edu.au/news-opinion/news/2023/02/28/how-to-perfect-your-prompt-writing-for-ai-generators.html)
+- [How to write good prompts](https://andymatuschak.org/prompts)
+- [If I Was Starting Prompt Engineering in 2023: My 8 Insider Tips](https://youtube.com/watch?v=SirW7feTjh0&feature=shares)
+- [Indirect Prompt Injection on Bing Chat](https://greshake.github.io/)
+- [Interactive guide to GPT-3 prompt parameters](https://sevazhidkov.com/interactive-guide-to-gpt-3-prompt-parameters)
+- [Introduction to ChatGPT](https://www.edx.org/course/introduction-to-chatgpt)
+- [Introduction to Reinforcement Learning with Human Feedback](https://www.surgehq.ai/blog/introduction-to-reinforcement-learning-with-human-feedback-rlhf-series-part-1)
+- [In defense of prompt engineering](https://simonwillison.net/2023/Feb/21/in-defense-of-prompt-engineering/)
+- [JailBreaking ChatGPT: Everything You Need to Know](https://metaroids.com/learn/jailbreaking-chatgpt-everything-you-need-to-know/)
+- [Language Models and Prompt Engineering: Systematic Survey of Prompting Methods in NLP](https://youtube.com/watch?v=OsbUfL8w-mo&feature=shares)
+- [Language Model Behavior: A Comprehensive Survey](https://arxiv.org/abs/2303.11504)
+- [Learn Prompting](https://learnprompting.org)
+- [Learning Prompt](https://github.com/thinkingjimmy/Learning-Prompt)
+- [Meet Claude: Anthropic’s Rival to ChatGPT](https://scale.com/blog/chatgpt-vs-claude)
+- [Methods of prompt programming](https://generative.ink/posts/methods-of-prompt-programming)
+- [Mysteries of mode collapse](https://www.lesswrong.com/posts/t9svvNPNmFf5Qa3TA/mysteries-of-mode-collapse)
+- [NLP for Text-to-Image Generators: Prompt Analysis](https://heartbeat.comet.ml/nlp-for-text-to-image-generators-prompt-analysis-part-1-5076a44d8365)
+- [NLP with Deep Learning CS224N/Ling284 - Lecture 11: Promting, Instruction Tuning, and RLHF](http://web.stanford.edu/class/cs224n/slides/cs224n-2023-lecture11-prompting-rlhf.pdf)
+- [Notes for Prompt Engineering by sw-yx](https://github.com/sw-yx/ai-notes)
+- [On pitfalls (and advantages) of sophisticated large language models](https://arxiv.org/abs/2303.17511)
+- [OpenAI Cookbook](https://github.com/openai/openai-cookbook)
+- [OpenAI Prompt Examples for several applications](https://platform.openai.com/examples)
+- [Pretrain, Prompt, Predict -  A New Paradigm for NLP](http://pretrain.nlpedia.ai)
+- [Prompt Engineer: Tech's hottest job title?](https://www.peoplematters.in/article/talent-management/is-prompt-engineering-the-hottest-job-in-ai-today-37036)
+- [Prompt Engineering by Lilian Weng](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/)
+- [Prompt Engineering 101 - Introduction and resources](https://www.linkedin.com/pulse/prompt-engineering-101-introduction-resources-amatriain)
+- [Prompt Engineering 101: Autocomplete, Zero-shot, One-shot, and Few-shot prompting](https://youtube.com/watch?v=v2gD8BHOaX4&feature=shares)
+- [Prompt Engineering 101](https://humanloop.com/blog/prompt-engineering-101)
+- [Prompt Engineering - A new profession ?](https://www.youtube.com/watch?v=w102J3_9Bcs&ab_channel=PatrickDebois)
+- [Prompt Engineering by co:here](https://docs.cohere.ai/docs/prompt-engineering)
+- [Prompt Engineering by Microsoft](https://microsoft.github.io/prompt-engineering)
+- [Prompt Engineering: The Career of Future](https://shubhamsaboo111.medium.com/prompt-engineering-the-career-of-future-2fb93f90f117)
+- [Prompt engineering davinci-003 on our own docs for automated support (Part I)](https://www.patterns.app/blog/2022/12/21/finetune-llm-tech-support)
+- [Prompt Engineering Guide: How to Engineer the Perfect Prompts](https://richardbatt.co.uk/prompt-engineering-guide-how-to-engineer-the-perfect-prompts)
+- [Prompt Engineering in GPT-3](https://www.analyticsvidhya.com/blog/2022/05/prompt-engineering-in-gpt-3)
+- [Prompt Engineering Template](https://docs.google.com/spreadsheets/d/1-snKDn38-KypoYCk9XLPg799bHcNFSBAVu2HVvFEAkA/edit#gid=0)
+- [Prompt Engineering Topic by GitHub](https://github.com/topics/prompt-engineering)
+- [Prompt Engineering: The Ultimate Guide 2023 [GPT-3 & ChatGPT]](https://businessolution.org/prompt-engineering/)
+- [Prompt Engineering: From Words to Art](https://www.saxifrage.xyz/post/prompt-engineering)
+- [Prompt Engineering with OpenAI's GPT-3 and other LLMs](https://youtube.com/watch?v=BP9fi_0XTlw&feature=shares)
+- [Prompt injection attacks against GPT-3](https://simonwillison.net/2022/Sep/12/prompt-injection)
+- [Prompt injection to read out the secret OpenAI API key](https://twitter.com/ludwig_stumpp/status/1619701277419794435?s=20&t=GtoMlmYCSt-UmvjqJVbBSA)
+- [Prompting: Better Ways of Using Language Models for NLP Tasks](https://thegradient.pub/prompting/)
+- [Prompting for Few-shot Learning](https://www.cs.princeton.edu/courses/archive/fall22/cos597G/lectures/lec05.pdf)
+- [Prompting in NLP: Prompt-based zero-shot learning](https://savasy-22028.medium.com/prompting-in-nlp-prompt-based-zero-shot-learning-3f34bfdb2b72)
+- [Prompting Methods with Language Models and Their Applications to Weak Supervision](https://snorkel.ai/prompting-methods-with-language-models-nlp)
+- [Prompts as Programming by Gwern](https://www.gwern.net/GPT-3#prompts-as-programming)
+- [Prompts for communicators using the new AI-powered Bing](https://blogs.microsoft.com/blog/2023/03/16/prompts-for-communicators-using-the-new-ai-powered-bing/)
+- [Reverse Prompt Engineering for Fun and (no) Profit](https://lspace.swyx.io/p/reverse-prompt-eng)
+- [Retrieving Multimodal Information for Augmented Generation: A Survey](https://arxiv.org/pdf/2303.10868.pdf)
+- [So you want to be a prompt engineer: Critical careers of the future](https://venturebeat.com/ai/so-you-want-to-be-a-prompt-engineer-critical-careers-of-the-future/)
+- [Simulators](https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators)
+- [Start with an Instruction](https://beta.openai.com/docs/quickstart/start-with-an-instruction)
+- [Talking to machines: prompt engineering & injection](https://artifact-research.com/artificial-intelligence/talking-to-machines-prompt-engineering-injection)
+- [Tech’s hottest new job: AI whisperer. No coding required](https://www.washingtonpost.com/technology/2023/02/25/prompt-engineers-techs-next-big-job/)
+- [The Book - Fed Honeypot](https://fedhoneypot.notion.site/25fdbdb69e9e44c6877d79e18336fe05?v=1d2bf4143680451986fd2836a04afbf4)
+- [The ChatGPT Prompt Book](https://docs.google.com/presentation/d/17b_ocq-GL5lhV_bYSShzUgxL02mtWDoiw9xEroJ5m3Q/edit#slide=id.gc6f83aa91_0_79)
+- [The ChatGPT list of lists: A collection of 3000+ prompts, examples, use-cases, tools, APIs, extensions, fails and other resources](https://medium.com/mlearning-ai/the-chatgpt-list-of-lists-a-collection-of-1500-useful-mind-blowing-and-strange-use-cases-8b14c35eb)
+- [The Most Important Job Skill of This Century](https://www.theatlantic.com/technology/archive/2023/02/openai-text-models-google-search-engine-bard-chatbot-chatgpt-prompt-writing/672991/)
+- [The Mirror of Language](https://deepfates.com/the-mirror-of-language)
+- [The Waluigi Effect (mega-post)](https://www.lesswrong.com/posts/D7PumeYTDPfBTp3i7/the-waluigi-effect-mega-post)
+- [Thoughts and impressions of AI-assisted search from Bing](https://simonwillison.net/2023/Feb/24/impressions-of-bing/)
+- [Unleash Your Creativity with Generative AI: Learn How to Build Innovative Products!](https://youtube.com/watch?v=jqTkMpziGBU&feature=shares)
+- [Unlocking Creativity with Prompt Engineering](https://youtube.com/watch?v=PFsbWAC4_rk&feature=shares)
+- [Using GPT-Eliezer against ChatGPT Jailbreaking](https://www.alignmentforum.org/posts/pNcFYZnPdXyL2RfgA/using-gpt-eliezer-against-chatgpt-jailbreaking)
+- [What Is ChatGPT Doing … and Why Does It Work?](https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/)
+- [Why is ChatGPT so good?](https://scale.com/blog/chatgpt-reinforcement-learning)
+- [【徹底解説】これからのエンジニアの必携スキル、プロンプトエンジニアリングの手引「Prompt Engineering Guide」を読んでまとめてみた](https://dev.classmethod.jp/articles/how-to-design-prompt-engineering/)

pages/risks.es.mdx

@@ -0,0 +1,11 @@
+# Riesgos y Malos Usos
+
+import { Callout } from 'nextra-theme-docs'
+
+Ya hemos visto lo efectivos que pueden ser los prompts bien elaborados para varias tareas utilizando técnicas como el aprendizaje de pocos ejemplos (few-shot learning) y el prompting encadenado (chain-of-thought prompting). A medida que pensamos en construir aplicaciones del mundo real sobre modelos de lenguaje de gran escala (LLMs, por sus siglas en inglés), se vuelve crucial pensar en los abusos, riesgos y prácticas de seguridad involucradas con los modelos de lenguaje.
+
+Esta sección se enfoca en destacar algunos de los riesgos y abusos de los LLMs a través de técnicas como la inyección de prompts. También destaca comportamientos dañinos y cómo mitigarlos potencialmente mediante técnicas de prompting efectivas. Otros temas de interés incluyen la generalización, la calibración, los sesgos, los sesgos sociales y la veracidad, por nombrar algunos.
+
+<Callout emoji="⚠️">
+Esta sección está en pleno desarrollo.
+</Callout>

pages/techniques.es.mdx

@@ -0,0 +1,5 @@
+# Técnicas de prompting
+
+A estas alturas, debería ser obvio que mejorar los prompts ayuda a obtener mejores resultados en diferentes tareas. Esa es la idea principal detrás de la ingeniería de prompts.
+
+Si bien los ejemplos básicos fueron divertidos, en esta sección cubriremos técnicas más avanzadas de ingeniería de prompts que nos permiten lograr tareas más complejas e interesantes.

pages/tools.es.mdx

@@ -0,0 +1,41 @@
+# Herramientas y Librerias
+#### (Ordenadas por nombre)
+
+- [AI Test Kitchen](https://aitestkitchen.withgoogle.com)
+- [betterprompt](https://github.com/krrishdholakia/betterprompt)
+- [ChatGPT Prompt Generator](https://huggingface.co/spaces/merve/ChatGPT-prompt-generator)
+- [ClickPrompt](https://github.com/prompt-engineering/click-prompt)
+- [DreamStudio](https://beta.dreamstudio.ai)
+- [DUST](https://dust.tt)
+- [Dyno](https://trydyno.com)
+- [EmergentMind](https://www.emergentmind.com)
+- [EveryPrompt](https://www.everyprompt.com)
+- [GPT Index](https://github.com/jerryjliu/gpt_index)
+- [GPTTools](https://gpttools.com/comparisontool)
+- [hwchase17/adversarial-prompts](https://github.com/hwchase17/adversarial-prompts)
+- [Interactive Composition Explorer](https://github.com/oughtinc/ice)
+- [LangChain](https://github.com/hwchase17/langchain)
+- [Lexica](https://lexica.art)
+- [loom](https://github.com/socketteer/loom)
+- [Metaprompt](https://metaprompt.vercel.app/?task=gpt)
+- [OpenAI Playground](https://beta.openai.com/playground)
+- [OpenICL](https://github.com/Shark-NLP/OpenICL)
+- [OpenPrompt](https://github.com/thunlp/OpenPrompt)
+- [OpenPlayground](https://nat.dev/)
+- [Playground](https://playgroundai.com)
+- [Prodia](https://app.prodia.com/#/)
+- [Prompt Base](https://promptbase.com)
+- [Prompt Engine](https://github.com/microsoft/prompt-engine)
+- [Prompt Generator for OpenAI's DALL-E 2](http://dalle2-prompt-generator.s3-website-us-west-2.amazonaws.com)
+- [Promptable](https://promptable.ai)
+- [PromptInject](https://github.com/agencyenterprise/PromptInject)
+- [Prompts.ai](https://github.com/sevazhidkov/prompts-ai)
+- [Promptmetheus](https://promptmetheus.com)
+- [PromptPerfect](https://promptperfect.jina.ai/)
+- [Promptly](https://trypromptly.com/)
+- [PromptSource](https://github.com/bigscience-workshop/promptsource)
+- [Promptist](https://promptist.herokuapp.com/)
+- [Scale SpellBook](https://scale.com/spellbook)
+- [sharegpt](https://sharegpt.com)
+- [ThoughtSource](https://github.com/OpenBioLink/ThoughtSource)
+- [Visual Prompt Builder](https://tools.saxifrage.xyz/prompt)