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Cms/calinator444/ssw.rules.content/rule/choosing large language models/rule #9819
Cms/calinator444/ssw.rules.content/rule/choosing large language models/rule #9819
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Co-authored-by: Seth Daily [SSW] <[email protected]>
Co-authored-by: Seth Daily [SSW] <[email protected]>
Co-authored-by: Seth Daily [SSW] <[email protected]>
Co-authored-by: Seth Daily [SSW] <[email protected]>
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LGTM
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When building an AI-powered solution, developers have access to a wide variety of Large Language Models (LLMs), such as Llama, GPT-4o, and Mistral. However, tools exist to evaluate these models in development **for free**, especially when integrating them into an application. |
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When building an AI-powered solution, developers have access to a wide variety of Large Language Models (LLMs), such as Llama, GPT-4o, and Mistral. However, tools exist to evaluate these models in development **for free**, especially when integrating them into an application. | |
When building an AI-powered solution, developers have access to a wide variety of Large Language Models (LLMs), such as Llama, GPT-4o, and Mistral. Tools exist to evaluate these models in development **for free**. |
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## The challenge of trying out AI models | ||
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Many AI providers charge per API call, making it expensive to experiment with different models. |
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Many AI providers charge per API call, making it expensive to experiment with different models. | |
Developing with AI can be prohibitive for a few reasons. |
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Many AI providers charge per API call, making it expensive to experiment with different models. | ||
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- **Decision Fatigue** There's a large number of Language models to choose from. The market size is expected to reach 13.52 billion in 2029. |
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- **Decision Fatigue** There's a large number of Language models to choose from. The market size is expected to reach 13.52 billion in 2029. | |
- **Decision Fatigue** There's a large number of Language models to choose from. The market size is expected to reach 13.52 billion US dollars by 2029. |
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- **Different Implementations** AI services generally provide their own API's with separate documentation for use. | ||
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- **Fine Tuning** Finding a cost to performance ratio for an LLM can be difficult to do. |
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- **Fine Tuning** Finding a cost to performance ratio for an LLM can be difficult to do. | |
- **Fine Tuning** Getting the right behaviour and performance by testing against various model parameters. |
✏️ Requested topic for Chewing The Fat
✏️ Added a new rule "Do you test before you invest In Large Language Models?"
✏️ @isaaclombardssw