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

Risks of New LLMs

Risks of New LLMs:

  1. High upfront costs and extended development time.

    • Explanation: Building a new model necessitates significant investments in terms of both money and time.
    • Example: A small e-health startup might exhaust its seed funding just developing an LLM for symptom analysis.

  2. Potential for underperformance if not correctly built or trained.

    • Explanation: Bespoke models carry the risk of not meeting expectations if not architected or trained adeptly.
    • Example: An insurance firm's in-house LLM might struggle with claims processing if not exposed to a diverse set of claim scenarios during training.

  3. Maintenance and updates.

    • Explanation: Custom LLMs require ongoing resources for upkeep and to stay current.
    • Example: A news analysis platform might need to constantly refine its LLM to handle evolving journalistic styles or emerging slang.

  4. Limited support and community.

    • Explanation: Bespoke tools don’t benefit from the broad community support that popular models enjoy.
    • Example: Developers at a niche e-commerce retailer might find themselves isolated when troubleshooting their custom LLM's glitches.

  5. Complex integration with other systems.

    • Explanation: Custom-built models might pose unforeseen integration challenges.
    • Example: A travel agency's bespoke LLM might not interface smoothly with global flight booking systems.

  6. Knowledge transfer and documentation challenges.

    • Explanation: The expertise to operate or refine the model might be concentrated among a few team members.
    • Example: If key AI team members of a music streaming company leave, the LLM's knowledge base might become hard to decipher for newcomers.

  7. Risk of technological obsolescence.

      • Explanation: A custom LLM might become outdated if not constantly aligned with AI advancements.
      • Example: An automotive firm's in-car LLM assistant might quickly seem archaic compared to competitors' offerings if not regularly updated.