Smaller and more efficient LLMs that have reasonably good reasoning capabilities can be fine-turned or complemented with RAG and incorporated into the workflow to automate these use cases. Using GPT-4 or some other very large LLM will become cost-prohibitive in these cases. /14
@abacusai
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Specialized Large-Scale Enterprise Use Cases with Custom Knowledge Bases
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Specialized large-scale use cases on custom knowledge bases This is the category of custom enterprise use-cases, where you may have several thousands of calls per day and the LLM needs to have an understanding of a custom knowledgebase or task. /13
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Price-Competitive API Calls for General-Purpose LLM Use Cases
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General-purpose use-case embedded in a product or service For these, a vanilla API call to SOTA LLM is sufficient. Price will be the key consideration here and as long as the large LLM providers have very competitive prices, making simple calls to their APIs will work. /12
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Future AI Services Market: Limited Competition with Subscription Models
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It is extremely unlikely that we will have more than 2-3 of these services. These services, like ChatGPT, will have a free and paid subscription tier. Paying subscribers will enjoy premium features like personalized responses and access to multi-modal features etc. /10
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Enterprise AI and LLM APIs: Two Classes of Business Use Cases
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Enterprise AI and LLM APIs The other big category of LLM use cases is businesses using these LLMs in their core products, services, and business processes. There are 2 classes of use cases in this space. /11
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Google, Meta, and OpenAI dominate consumer LLM market
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All this means that we will end up with Google, Meta, and potentially OpenAI being the key players in the consumer LLM (e.g., ChatGPT, Bard, etc.) world. /9
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GPT-4 Outperforms Specialized LLMs Across Tasks
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This is unlikely to be the case for general-purpose tasks. Large SOTA LLMs outperform specialized LLMs in most tasks. Again GPT-4 outperforms specialized LLMs on pretty much everything from code generation to writing and reasoning tasks. /7
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General-Purpose vs. Specialized Tasks: When Fine-Tuning and RAG Matter
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It's important to draw the distinction between general-purpose tasks like Python code generation vs. specialized tasks like having knowledge of the Abacus platform. The former typically DOES NOT require fine-tuning or RAG, while the latter requires some custom work. /8
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Bard vs GPT-4: Performance on Recent vs Historical Data
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Already, Bard outperforms GPT-4 when it comes to queries about recent data and GPT-4 does much better on queries on data available before September 2021 (it's training cut-off date). /5
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Will Specialized LLMs Emerge for Specific General-Purpose Tasks?
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LLMs For General Purpose Tasks So the next question is will we have specialized LLMs for some general-purpose tasks like coding, reasoning, summarization, or writing? GPT-4 does really well on code compared to Google's LLMs, so will there be several purpose-built LLMs? /6