30 Days of LLMs: Day 17 — Implementing LLM QA Chains
For Day 17 of the W&B 30 Days of LLMs, we get into the intricacies of applying retrieval and question-answering (QA) frameworks in Large Language Model (LLM) applications. Enroll today and get started!
Created on December 8|Last edited on December 10
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Day 17 — Implementing LLM QA Chains
In Day 17 of the Weights & Biases 30 Days of LLMs, we delve into the intricacies of applying retrieval and question-answering (QA) frameworks in Large Language Model (LLM) applications. Accompany Darek Kleczek, a Machine Learning Engineer at Weights & Biases, as he explores the detailed process of document parsing and retrieval utilizing the Langchain library and Chroma vector store.
Chapter Highlights
- Step-by-Step Retrieval Methods: Discover the detailed methodology for embedding documents and extracting pertinent sections using embedding models and vector databases.
- Document Processing with Langchain: Learn about the Langchain library's role in streamlining document processing and parsing, enhancing the efficiency of your workflow.
- Crafting QA Chains for Accuracy: Gain insights into constructing QA chains that merge user inquiries with contextually appropriate documents to produce precise LLM answers.
- Interactive Learning via Jupyter Notebook: Engage in an interactive experience using a Jupyter notebook to practically apply these techniques.
- Tracking and Logging with Weights & Biases: Observe the integration of Weights & Biases in documenting and monitoring experiments for improved analysis and refinement.
Key Course Information
- No deep machine learning knowledge is needed, just some familiarity with Python programming.
- Strategies for continual enhancement of your LLM applications.
- Unique perspectives on the LLM tools used by Weights & Biases.
Free Enrollment
Preview of the Next Chapter
Don't miss our next chapter, which focuses on further enhancing and refining LLM applications.
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