30 Days of LLMs: Day 14 — Advanced Architectures for LLM Apps - Integrating Embeddings
On Day 14 of our 30 Days of LLMs, we explore embedding models & vector databases to boost LLM capabilities. Delve into sophisticated application architectures and semantic search techniques!
Created on December 8|Last edited on December 10
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Day 14: Advanced Architectures for LLM Apps — Integrating Embeddings
Welcome to Day 14 of the Weights & Biases 30 Days of LLMs. We're continuing with our complimentary course "Building LLM-Powered Apps." Join our Machine Learning Engineer Darek Kleczek, as he delves into combining embedding models with document stores to boost LLM capabilities.
Chapter Highlights
- Transitioning to Application Development: Move from basic API usage to the intricate process of creating a fully operational LLM-based app.
- Addressing API Constraints: Grasp the limitations inherent in using LLM APIs for generating responses and the occurrence of hallucinations in the model's output.
- Examining Real-world Examples: Explore practical examples from our synthetic dataset to highlight discrepancies in LLM-generated responses.
- Understanding Knowledge Limits and Hallucinations: Investigate the challenges of knowledge cutoffs in LLMs and the occurrence of hallucinations in the responses they generate.
- Insights into App Architecture: Begin the process of constructing an LLM application's architecture, addressing various challenges and identifying possible solutions.
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.
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Preview of the Next Chapter
Join us tomorrow, which focuses on enhancing and fine-tuning LLM applications.
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