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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 with Existing LLMs

Risks with Existing LLMs:

  1. Might not match the efficiency of a specialized model.

    • Explanation: Pre-trained models cater to broad applications and might not be fine-tuned for niche tasks.
    • Example: A digital archaeology firm may find that ChatGPT struggles with accurately translating ancient scripts compared to a dedicated model.

  2. Potential for biased or inappropriate outputs.

    • Explanation: General models can inadvertently generate outputs reflecting biases present in their vast training data.
    • Example: A cultural sensitivity tool relying on an existing LLM might occasionally miss or misinterpret nuances specific to certain regions.

  3. Dependency on external developments.

    • Explanation: Relying on third-party models means being subject to their development cycle and priorities.
    • Example: An e-learning platform using ChatGPT for course creation might have to adapt if a key feature they rely on is deprecated.

  4. Integration challenges.

    • Explanation: Existing LLMs may not always mesh perfectly with proprietary software or platforms.
    • Example: A proprietary CRM system might encounter hiccups when integrating real-time language translation from a pre-existing LLM.

  5. Less differentiation in the market.

    • Explanation: Using common models might not offer a unique value proposition.
    • Example: A content-generation startup using a widely adopted LLM might struggle to convince clients of its distinct advantage.

  6. Opaque model internals.

    • Explanation: It's challenging to understand the intricate workings or decision criteria of large, pre-trained models.
    • Example: A financial institution might find it difficult to explain specific AI-driven investment advice to stakeholders.

  7. Potential costs with scaling.

    • Explanation: Licensing or API access fees might escalate with increased usage.
    • Example: A booming online marketplace might see its overheads spike if their LLM-driven customer support chat logs more interactions.