Saved by Sam Liebeskind
Using LLMs to support online communities
By grounding LLMs with use-case-specific information through RAG, the quality and accuracy of responses are improved.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
ANY
LLM of your choice, statistical methods, or NLP models that runs
locally on your machine
:
- G-Eval
- Summarization
- Answer Relevancy
- Faithfulness
- Contextual Recall
- Contextual Precision
- RAGAS
- Hallucination
- Toxicity
- Bias
- etc.
GitHub - confident-ai/deepeval: The LLM Evaluation Framework
“The mark of a good knowledge management system is it’s able to surprise you and it’s able to be a good conversational partner…it’s reminding you things you thought before” – Conor White-Sullivan
Erik Torenberg • Village Global's Venture Stories on Apple Podcasts
When people engage with LLMs, they ask for suggestions, refer to shared history and context, and build on previous responses. Their top expressed need is for systems to have a back-and-forth with them, to help them learn, to help them narrow things down, and to let them know what the system needs to provide better responses.
Michal Lahav • From Prompt Engineering to Collaborating: A Human-Centered ...
LLMs augment software products in two fundamental ways – they enable:
i) better understanding and processing of unstructured data;
ii) more scalable creation of content and work product.
i) better understanding and processing of unstructured data;
ii) more scalable creation of content and work product.
Chris Rainville • LLM agents: the next platform shift in B2B software
Because if you strip LLMs to their essence, they are just a much better way of using statistics to aggregate human intelligence and connect everything we’ve all done together to get more use out of it.