Advanced Retrieval for AI with Chroma
Free for a limited time
- Learn to recognize when queries are producing poor results.
- Learn to use a large language model (LLM) to improve your queries.
- Learn to fine-tune your embeddings with user feedback.
What you’ll learn in this course
Information Retrieval (IR) and Retrieval Augmented Generation (RAG) are only effective if the information retrieved from a database as a result of a query is relevant to the query and its application.
Too often, queries return semantically similar results but don’t answer the question posed. They may also return irrelevant material which can distract the LLM from the correct results.
This course teaches advanced retrieval techniques to improve the relevancy of retrieved results.
The techniques covered include:
- Query Expansion: Expanding user queries improves information retrieval by including related concepts and keywords. Utilizing an LLM makes this traditional technique even more effective. Another form of expansion has the LLM suggest a possible answer to the query which is then included in the query.
- Cross-encoder reranking: Reranking retrieval results to select the results most relevant to your query improves your results.
- Training and utilizing Embedding Adapters: Adding an adapter layer to reshape embeddings can improve retrieval by emphasizing elements relevant to your application.
Who should join?
Anyone who has intermediate Python knowledge and wants to learn advanced retrieval techniques for retrieving data from their vector database.
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