Stop Building Bad RAG: Advanced Chunking & Pre-Retrieval on AWS Bedrock

Описание: Are your Retrieval-Augmented Generation (RAG) applications retrieving the wrong context, losing details, or hallucinating? The secret to a production-grade AI application isn't just the LLM—it's exactly how you prepare and chunk your data! 🚀
In this video, we dive deep into the ultimate RAG optimization techniques: Pre-Retrieval Strategies and Advanced Chunking Methods. Plus, we walk you through exactly how to implement these strategies natively in Amazon Bedrock Knowledge Bases, including a step-by-step look at building a fully custom chunking pipeline using AWS Lambda!
📌 What You Will Learn:
Pre-Retrieval Optimization: How to use Query Expansion and Query Decomposition to fix vague user prompts before searching your vector database.
Chunking Theory: The pros and cons of Fixed-size, Recursive, Semantic, and Hierarchical chunking strategies.
AWS Native Chunking: How Amazon Bedrock Knowledge Bases manages default, fixed, hierarchical, and semantic chunking out of the box.
Custom Pipeline Implementation: How to bypass default limits by using an AWS Lambda Transformation function to inject custom logic (like LangChain text splitters) into your Bedrock ingestion pipeline.
🔔 If you found this video helpful, please hit the LIKE button, SUBSCRIBE to the channel, and turn on notifications so you don't miss our upcoming deep dives into Enterprise AI! Let us know in the comments which chunking strategy you are using for your RAG applications!
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