Summary LangChain "RAG Evaluation" Webinar - YouTube (Youtube) www.youtube.com
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LangChain conducted a webinar on improving evaluation processes and reliable app development, addressing biases in validation metrics and the use of annotated answers to identify missing data points.
Slides
Slide Presentation (13 slides)
Key Points
- The LangChain "RAG Evaluation" webinar discusses the evaluation process and improvements made to the evaluation framework.
- LangChain conducted an evaluation of their open consult framework and identified inadequate metrics for validation.
- Improving retrieval techniques and enhancing embeddings is important for context relevancy.
- Customizing the evaluation process to fit specific criteria and user considerations is emphasized.
- User feedback, such as thumbs up and thumbs down, is important for identifying issues with the model's retrieval.
Summaries
44 word summary
LangChain conducted a webinar on evaluating RAG applications, emphasizing the need for improved evaluation processes and the use of engineering disciplines for reliable app development. They also discussed biases in existing validation metrics and the use of annotated answers to identify missing data points.
45 word summary
LangChain conducted a webinar on evaluating RAG applications, focusing on improving the evaluation process and using engineering disciplines for reliable app development. They highlighted biases present in existing metrics for validation. The speaker discussed the use of annotated answers to identify missing data points and
426 word summary
This is a webinar on evaluating RAG applications, featuring guests from the Reg team and LangChain. The webinar will be recorded and available on YouTube. The format includes introductions, a presentation on what the Reg team is building, an overview of Lang
LangChain conducted an evaluation of their open consult framework and found that existing metrics for validation were inadequate. They focused on improving the evaluation process and emphasized the importance of using engineering disciplines for reliable and frictionless app development. They also highlighted the biases present in
The excerpt is from a webinar discussing the LangChain "RAG Evaluation" project. The speaker explains that they use annotated answers to identify data points that are present in the context but missing from the retrieve context. They calculate a finance score based on the
Improving retrieval techniques and enhancing embeddings is important for context relevancy. However, it can cause issues with low context and association. Adjusting the moment and transforming the prompt can improve relevancy. Locksmith provides good visibility and tracing for evaluation. Metrics
In a LangChain "RAG Evaluation" webinar, the speaker discusses the integration of Rag and the experiments conducted. One highlight is the Cookbook, which provides information on how to get started. The speaker shares a notebook that demonstrates a simpler metric for measuring
The LangChain "RAG Evaluation" webinar discussed the process of passing information through the evaluator and how decisions are made. The presenter emphasized the importance of customizing the evaluation process to fit specific criteria and user considerations. The option to make changes to prompts
In this excerpt from the LangChain "RAG Evaluation" webinar, the speaker discusses their approach to optimizing the retrieval system. They found that users are generally satisfied when the model captures the right chunk and when the chunk is of sufficient size to provide a
LangChain conducted a webinar discussing the evaluation of their RAG model. Pedro mentioned that user feedback, such as thumbs up and thumbs down, is important for identifying issues with the model's retrieval. When a thumbs down is received, they analyze the retrieval
During the LangChain "RAG Evaluation" webinar, the speakers discussed various issues related to embedding and retrieval of information. They emphasized that context-free calls are important when answering questions because they do not contain the necessary information. It was also mentioned that video
During the LangChain "RAG Evaluation" webinar, Pedro discusses how Noah can answer questions that require aggregating data from different documents. He explains that by increasing the number of chunks retrieved, the chances of getting relevant information from multiple documents are maximized
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Source: https://www.youtube.com/watch?v=fWC4VxolWAk
Page title: LangChain "RAG Evaluation" Webinar - YouTube