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are focused both on the new "ware" ("middleware" and "software" that will wrap and integrate LLMs), as well as building high-quality
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automation-focused enterprise RAG models.
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[BLING](https://medium.com/@darrenoberst/small-instruct-following-llms-for-rag-use-case-54c55e4b41a8) |
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[RAG-INSTRUCT-TEST-DATASET](https://medium.com/@darrenoberst/how-accurate-is-rag-8f0706281fd9) |
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[LLMWARE EMERGING STACK](https://medium.com/@darrenoberst/the-emerging-llm-stack-for-rag-deee093af5fa) |
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are focused both on the new "ware" ("middleware" and "software" that will wrap and integrate LLMs), as well as building high-quality
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automation-focused enterprise RAG models.
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Our latest initiative is the SLIM model portfolio (Structured Language Instruction Models) - check out how to use in multi-model, Agent-based workflows
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--[SLIM Agents and Multi-Step RAG](https://www.youtube.com/watch?v=cQfdaTcmBpY)
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--[Using SLIM examples](https://github.com/llmware-ai/llmware/tree/main/examples/SLIM-Agents/)
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Please check out a few of our recent blog postings related to these initiatives:
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[BLING](https://medium.com/@darrenoberst/small-instruct-following-llms-for-rag-use-case-54c55e4b41a8) |
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[RAG-INSTRUCT-TEST-DATASET](https://medium.com/@darrenoberst/how-accurate-is-rag-8f0706281fd9) |
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[LLMWARE EMERGING STACK](https://medium.com/@darrenoberst/the-emerging-llm-stack-for-rag-deee093af5fa) |
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