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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 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.