Data Availability:
By understanding data availability and its alignment with your goals, you can make informed decisions on whether to leverage existing LLMs or invest in custom solutions.
Existing LLMs:
Function effectively when tasks align with the broad and diverse datasets on which they've been trained.
- General Data Pools:
- Explanation: Existing LLMs are trained on vast amounts of publicly available text from the internet, including books, articles, and websites.
- Example: A company wants to create a chatbot to answer general knowledge questions for a trivia game. Since most of these questions likely touch upon common knowledge and popular topics, an existing LLM trained on broad datasets would be sufficient.
- Public Domains & Common Topics:
- Explanation: Topics frequently discussed in public domains or on the internet are already part of the LLM's training data.
- Example: A startup aims to design an app offering daily news summaries. Given that news topics are widely covered on the internet, an existing LLM can easily generate these summaries without needing additional specific training.
- Multiple Languages & Cultures:
- Explanation: Prominent LLMs have been trained on data from multiple languages and cultures, making them adept at understanding and generating content in various languages.
- Example: A travel agency is building a global platform and needs to provide cultural tips and phrases for tourists. Leveraging an existing LLM, they can produce culturally relevant content in multiple languages without additional training.
New LLM:
Necessary when tasks require specialized datasets not covered by existing LLMs or when proprietary data is involved.
- Niche or Proprietary Datasets:
- Explanation: Some sectors have specific data which isn't publicly accessible or widely discussed. For these, a custom LLM might be necessary.
- Example: An aerospace company has decades of proprietary research on a particular kind of propulsion technology. To build an AI assistant for their engineers, they would need a custom LLM trained on their internal datasets.
- Data Privacy & Sensitivity:
- Explanation: Tasks that involve sensitive data, where companies are reluctant to use cloud-based LLMs due to privacy concerns, might necessitate a bespoke solution.
- Example: A healthcare institution wants to process patient records to generate treatment insights. Given the sensitive nature of medical records, a custom LLM, built and maintained in a secure environment, would be more appropriate than a public model.
- Highly Specialized Domains:
- Explanation: Domains with specialized knowledge that aren't commonly found in public datasets require specific training.
- Example: A firm specializing in deep-sea exploration technologies, a topic not widely represented in general datasets, wants an LLM to assist in research documentation. A custom model, trained on specific marine research and technology documents, would be essential.