Ghost Drafter Delivers Commercial LLMs
for Authors, Editors, and Translators
Ghost Drafter provides a curated selection of Large Language Model (LLM) engines tailored to support learning and development professionals in crafting high-quality educational content, training materials, and personalized learning experiences. Each model offers unique advantages that enhance the learning process, from creating adaptive learning materials to enabling continuous, real-time learner interaction. Below are the featured LLM engines included in Ghost Drafter, with insights into their relevance for the L&D community.
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AI21 Jurassic 2
Jurassic 2 is an advanced LLM developed by AI21 Labs, trained on proprietary datasets. It offers multilingual capabilities, including English, Spanish, French, Portuguese, Italian, and Dutch. With a context window of 8K tokens, Jurassic 2 is ideal for generating detailed explanations, educational content, and assessments in multiple languages.
Why this is important: Multilingual support is critical in global training programs, allowing L&D professionals to create materials accessible to a diverse audience. Jurassic 2’s ability to handle large amounts of context makes it valuable for crafting comprehensive training modules or lessons that adapt to learners’ needs.
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Amazon Titan
Amazon’s Titan models are robust LLMs pre-trained on extensive datasets and support over 100 languages in preview mode. Titan offers a context window of 8K tokens, ideal for generating complex training documents and interactive learning content.
Why this is important: L&D professionals can leverage Titan’s language versatility to develop courses in multiple languages, ensuring inclusivity in global workforces. Titan’s support for rich, detailed context allows for the creation of personalized learning paths and more tailored, adaptive learning materials.
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Anthropic Claude
Claude is renowned for its ability to generate creative content and summaries with a focus on ethical output. It uses Constitutional AI principles, ensuring that content generated is non-toxic and adheres to ethical guidelines. Claude has a massive context window of 200K tokens, enabling it to handle extensive content.
Why this is important: The ability to maintain ethical and inclusive content creation makes Claude perfect for developing educational materials that align with organizational values. The large context window allows for the development of long-form, in-depth training guides or manuals, ensuring that learners can follow comprehensive learning paths without sacrificing depth or ethical quality.
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Google Gemini
Google’s Gemini is a cutting-edge LLM trained on diverse datasets and supports 38 languages. It outperforms human experts on complex reasoning tasks (MMLU), making it a top choice for problem-solving and knowledge assessments. It is well-suited for complex training scenarios with a context window of 32K tokens.
Why this is important:
In the L&D context, Gemini excels at creating assessments and training materials that not only test but also enhance learners’ problem-solving skills. Its large context window is useful for designing interactive simulations or complex case studies, which are valuable in corporate training and development.
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Meta Llama 2
Llama 2 is an open-source model trained on a vast dataset, fine-tuned to minimize harmful content. It has a context window of 4K tokens and is known for its adaptability. However, it may produce hallucinations, so its outputs should be validated when creating educational materials.
Why this is important:
Llama 2’s open-source nature makes it a cost-effective choice for educational institutions and L&D teams with limited resources. It is a good tool for experimenting with content generation for online courses, eLearning materials, or instructional documents, though L&D professionals should apply quality checks for accuracy.
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Mistral Large
Mistral Large supports five languages: English, French, Spanish, German, and Italian, with a context window of 32K tokens. Although it has a large context window, the cut-off date of its training data is unknown, though it is likely updated through 2024.
Why this is important:
The ability to generate content in multiple languages makes Mistral Large ideal for international corporate training programs. Its large context window allows for the creation of complex training scenarios, ensuring learners receive content that adapts to their specific learning paths.
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Mistral Mixtral 8x7B
Mixtral 8x7B utilizes a unique design that leverages a Sparse Mixture of Experts (SMoE) system, generating content by combining inputs from specialized models. With a context window of 4K tokens, Mixtral is adept at handling niche requests for educational content.
Why this is important:
Mixtral’s specialized model design is particularly useful in L&D, where different training materials may be required for specific job roles. By leveraging expertise from multiple domains, Mixtral can create role-based learning paths that ensure every learner receives content aligned with their needs and expertise level.
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OpenAI ChatGPT
ChatGPT is widely recognized for its versatility in generating content across various domains. Available in versions 3.5 Turbo, 4, and 4 Turbo, it supports a wide range of applications from text generation to reasoning tasks. The context windows vary from 4K to 128K tokens.
Why this is important:
ChatGPT’s adaptability makes it a valuable tool for instructional designers and trainers, enabling quick creation of learning content, quizzes, and interactive dialogue-based learning. The large context window in ChatGPT 4 Turbo allows for the development of comprehensive training manuals or interactive eLearning content that spans multiple topics.
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Perplexity AI
Perplexity AI stands out as an LLM that continuously updates itself through ongoing internet crawling, meaning it has no data cut-off date. Built on Mistral and Llama 2, Perplexity has a context window of 4K tokens, ensuring up-to-date content.
Why this is important:
For L&D professionals, the ability to generate real-time content is crucial for industries where the learning material must stay current, such as compliance training or ongoing professional development in fast-evolving sectors like technology or healthcare.
These LLM engines, embedded in Ghost Drafter, offer transformative potential for L&D professionals, enabling more personalized, multilingual, and adaptive learning experiences. Each model brings specific advantages that can be applied to optimize learning paths, improve content creation, and ensure ethical, up-to-date training for a global workforce.
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