- Challenges of Using Commercial LLM APIs: Commercial language models trained on internet data may provide irrelevant context and produce false information, limiting their effectiveness in querying internal knowledge bases. (#Chatbots #LLM #KnowledgeBase)
Limitations of Passing Entire Knowledge Base: Due to token limits, passing the entire knowledge base to LLM prompts is challenging and costly. Workarounds exist but may not suffice for larger knowledge bases, inflating API usage costs. (#TokenLimit #KnowledgeBase #Costs)
Alternative Approaches and Considerations: Exploring open-source models and fine-tuning them on internal data is an option, but it requires specialized talent, time, and can’t fully address hallucination challenges. In-house hosting adds to costs and resource requirements. (#OpenSource #FineTuning #Challenges)
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Please note that the above summary is not part of the original text and has been created to capture the key points of the article in a concise and engaging manner.
The article explores the challenges faced when building chatbots for internal knowledge bases using large language models (LLMs). It highlights the limitations of using commercial LLM APIs and the difficulties in passing the entire knowledge base to LLM prompts. Alternative approaches like open-source models and fine-tuning are discussed, along with their drawbacks. Cost, accuracy, and hallucination challenges are key considerations in implementing LLM-based chatbots for internal knowledge bases.
🍃 #Chatbots #LLM #KnowledgeBase #TokenLimit #Costs #OpenSource #FineTuning #Challenges