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Large Language Models (LLMs)

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      • Explanation: A custom LLM might become outdated if not constantly aligned with AI advancements.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.