Large Language Models (LLMs)

Overview How are LLM's built? Large Language Models Uses Use Existing LLMs or Build New LLM Benefits of Using Existing LLMs Benefits of Creating a New LLM Risks with Existing LLMs Risks of New LLMs Task Complexity - Existing LLMs Vs New LLM Data Availability - Existing LLMs vs New LLM Budget and Resources - Existing LLMs vs New LLM Expertise Requirement - Existing LLMs vs new LLM AI Services that are not LLM based

Expertise Requirement - Existing LLMs vs new LLM

Expertise Requirement:

 While existing LLMs offer the advantage of reduced technical depth and pre-existing support, custom-built LLMs require a significant depth of expertise, but provide unparalleled specificity and control.

 

 

Existing LLMs:

Best for businesses without extensive in-house expertise in AI and machine learning. Using existing solutions simplifies deployment and usage.

 

- Minimal Technical Overhead:

  - Explanation: Leveraging a pre-trained model means you mostly interface with user-friendly platforms and APIs, without diving deep into model intricacies.

  - Example: A local bookstore wants to implement a chatbot to answer customer queries. Using an existing LLM like ChatGPT, they can set it up with the platform's graphical interface, without needing to know the underlying machine learning mechanisms.

 

- Vendor Support:

  - Explanation: Established LLM providers typically offer comprehensive support, tutorials, and documentation, facilitating smoother integration.

  - Example: An online retailer integrating a product recommendation system based on an existing LLM might encounter some hurdles. However, they can rely on the LLM provider's customer service, potentially saving on hiring a specialized consultant which could cost $150/hour or more.

 

- Community & Pre-existing Solutions:

  - Explanation: Popular LLM platforms often have vast communities, which means there's a wealth of shared knowledge, tools, and plugins available.

  - Example: A startup wanting to enhance their mobile app's search functionality can find open-source tools or community-shared scripts tailored for existing LLMs, bypassing the need for a dedicated developer, who could cost upwards of $100,000/year in salary.

 

New LLM:

Tailored for organizations that possess (or can afford) niche AI expertise, enabling the creation of highly specialized solutions.

 

- Deep Technical Knowledge:

  - Explanation: Constructing a new LLM demands understanding the intricacies of neural networks, algorithms, and data science.

  - Example: A biotech company developing an LLM to interpret genetic data would require experts in bioinformatics and machine learning. Hiring such experts could cost anywhere from $120,000 to $200,000/year or more in salaries, given the niche expertise.

 

- Ongoing Model Management:

  - Explanation: Custom models require continuous refinement, retraining, and adjustment.

  - Example: A financial firm that develops an LLM for predicting market trends will need to adjust the model as economic landscapes shift, necessitating a dedicated team for model monitoring and updates. This team, comprising data scientists and market experts, could come at a combined annual salary cost of over $500,000.

 

- Research and Development:

  - Explanation: New LLMs might demand exploratory research, necessitating an R&D team.

  - Example: An aerospace company wants an LLM tailored for predicting satellite trajectory and space weather patterns. They'd invest in an R&D department, potentially setting up a lab with equipment and salaries costing upwards of $1 million annually.