9 Tips to Design Hallucination-Free RAG/LLM Systems
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9 Tips to Design Hallucination-Free RAG/LLM Systems
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And in our case (see 
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And in our case (see 
https://mltblog.com/4fPuvTb
https://mltblog.com/4fPuvTb
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), with no training and zero parameter! By zero parameter, I mean no neural network parameters (the typical 40B you see in many LLMs, that stands for 40 billion parameters also called weights). We do indeed have a few intuitive parameters that you can fine-tune in real time.
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), with no training and zero parameter! By zero parameter, I mean no neural network parameters (the typical 40B you see in many LLMs, that stands for 40 billion parameters also called weights). We do indeed have a few intuitive parameters that you can fine-tune in real time.
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Tips to make your system hallucination-free:
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Tips to make your system hallucination-free:
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- We use sub-LLMs specific to each topic (part of a large corpus), thus mixing unrelated items is much less likely to happen.
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- We use sub-LLMs specific to each topic (part of a large corpus), thus mixing unrelated items is much less likely to happen.
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- In the base version, the output returned is unaltered rather than reworded. The latter can cause hallucinations.
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- In the base version, the output returned is unaltered rather than reworded. The latter can cause hallucinations.
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- It shows a high-level structured summary first, with category, tags, agents attached to each item; the user can click on the items he is most interested in based on summary, reducing the risk of misfit.
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- It shows a high-level structured summary first, with category, tags, agents attached to each item; the user can click on the items he is most interested in based on summary, reducing the risk of misfit.
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- The user can specify agents, tags or categories in the UI, it's much more than a prompt box. He can also include negative keywords, joint keywords that must appear jointly in the corpus, put a higher weight on the first keyword in the prompt, or favor the most recent material in the results. 
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- The user can specify agents, tags or categories in the UI, it's much more than a prompt box. He can also include negative keywords, joint keywords that must appear jointly in the corpus, put a higher weight on the first keyword in the prompt, or favor the most recent material in the results. 
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- Python libraries can cause hallucinations. For instance, project and projected have the same stem. We use these libraries but with workarounds to avoid these issues that can lead to hallucinations. 
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- Python libraries can cause hallucinations. For instance, project and projected have the same stem. We use these libraries but with workarounds to avoid these issues that can lead to hallucinations. 
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- We return a relevancy score to each item in the prompt results, ranging from 0 to 10. If we cannot find highly relevant information in your augmented corpus, despite using a synonyms dictionary, the score will be low, telling you that the system knows that this particular item is not great. You can choose to no show items with a low score, though sometimes they contain unexpectedly interesting information (the reason to keep them).
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- We return a relevancy score to each item in the prompt results, ranging from 0 to 10. If we cannot find highly relevant information in your augmented corpus, despite using a synonyms dictionary, the score will be low, telling you that the system knows that this particular item is not great. You can choose to no show items with a low score, though sometimes they contain unexpectedly interesting information (the reason to keep them).
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- We show links and references, all coming from reliable sources. The user can double-check in case of doubt.
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- We show links and references, all coming from reliable sources. The user can double-check in case of doubt.
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- We suggest alternate keywords to use in your next prompts (related concept)
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- We suggest alternate keywords to use in your next prompts (related concept)
9 Tips to Design Hallucination-Free RAG/LLM Systems

And in our case (see https://mltblog.com/4fPuvTb), with no training and zero parameter! By zero parameter, I mean no neural network parameters (the typical 40B you see in many LLMs, that stands for 40 billion parameters also called weights). We do indeed have a few intuitive parameters that you can fine-tune in real time.

Tips to make your system hallucination-free:

- We use sub-LLMs specific to each topic (part of a large corpus), thus mixing unrelated items is much less likely to happen.

- In the base version, the output returned is unaltered rather than reworded. The latter can cause hallucinations.

- It shows a high-level structured summary first, with category, tags, agents attached to each item; the user can click on the items he is most interested in based on summary, reducing the risk of misfit.

- The user can specify agents, tags or categories in the UI, it's much more than a prompt box. He can also include negative keywords, joint keywords that must appear jointly in the corpus, put a higher weight on the first keyword in the prompt, or favor the most recent material in the results. 

- Python libraries can cause hallucinations. For instance, project and projected have the same stem. We use these libraries but with workarounds to avoid these issues that can lead to hallucinations. 

- We return a relevancy score to each item in the prompt results, ranging from 0 to 10. If we cannot find highly relevant information in your augmented corpus, despite using a synonyms dictionary, the score will be low, telling you that the system knows that this particular item is not great. You can choose to no show items with a low score, though sometimes they contain unexpectedly interesting information (the reason to keep them).

- We show links and references, all coming from reliable sources. The user can double-check in case of doubt.

- We suggest alternate keywords to use in your next prompts (related concept)
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