Generative AI adoption framework - 4 quardants - low risk item where quality of out of box LLm model is good, low risk item where quality need improvement, high risk item where better results are needed, high risk item where quality is very low. One should determine error rate and tolerance for error rate and accordingly implement generative AI.
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Additional Comments

Adoption for LLM in Enterprises
  • Adoption framwork help enterprises determine areas where Large Language Model adoption can happen quickly. Areas that are less mission crticial and where out of box LLM works well with low error rate are good candidate to onboard generative AI.
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  • Framework need to be structured. It should provide stages, best practices and how to evaluate risks and opportunties.
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  • Slides describe 5 stages of LLM adoption phases.

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  • It describe areas where out of box model can be used without issues. It describe areas where out of box model will not work and there is opportunity to innovate
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  • Slide also describe how to extend AI risk and consider all risks for generative AI
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  • Finally slide describe use caes that can be adopted with ease
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  • Mission critical areas where out of box model does not work - should be adopted at last stage. Companies should evaluate whether training on domain specific data will help. If building such model produce good result, it will provide competitive advantage
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    Factors to Consider for Generative AI Adoption
    • Data Availability: Assess the availability and quality of data required for training the generative AI model.
    • Computational Resources: Evaluate the computational resources needed to train and deploy the generative AI model.
    • Expertise and Skills: Determine the level of expertise and skills required to develop and maintain the generative AI model.
    • Ethical Considerations: Consider the ethical implications and potential biases associated with the generative AI model's outputs.
    • Legal and Regulatory Compliance: Ensure compliance with relevant laws and regulations when using generative AI.
    • Business Objectives: Align the adoption of generative AI with the organization's strategic goals and objectives.
    • Costs and Return on Investment: Evaluate the costs associated with implementing and maintaining generative AI, and assess the potential return on investment.
    • Security and Privacy: Address security and privacy concerns related to the data used and generated by the generative AI model.
    • User Acceptance: Consider the acceptance and usability of generative AI outputs by end-users or customers.

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    Blog 4

    From the blog

    Build Dataproducts

    How Dataknobs help in building data products

    Enterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product.

    Be Data Centric and well governed

    Generative AI is one of approach to build data product

    Generative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner.

    Be Data Centric and well governed

    Generative AI is one of approach to build data product


    Generative AI has enabled many transformative scenarios.
    We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products.
    We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner.

    Spotlight

    Generative AI slides

    Learn generative AI, LLM, foundation model
  • Learn generative AI - applications, LLM, architecture
  • See best practices for prompt engineering
  • Evaluate whether you should use out of box foundation model, fne tune or use in-context learning
  • Most important - be aware of concerns, issues, challenges, risk of genAI and LLM
  • See vendor comparison - Azure, OpenAI, GCP, Bard, Anthropic. Review framework for cost computation for LLM
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