AWS Bedrock RAG Demo in Python (Embeddings + Similarity Search) | AIF-C01 Ep 6

AWS Certified AI Practitioner (AIF-C01) – Episode 6: AWS Bedrock RAG Demo (Python)
Timestamps:
0:00 Demo Prerequisites
3:20 LLM Models Used
8:00 RAG Code Explanation(Python)
18:00 RAG in Action!!
21:54 Exam Summary & Questions

In this hands-on episode, we build a Mini-RAG pipeline end-to-end using Amazon Bedrock:
✅ Create embeddings with Titan Text Embeddings v2
✅ Perform similarity search (nearest match using cosine similarity)
✅ Use Llama 3 Instruct to generate a grounded answer from retrieved context
✅ Understand exactly how RAG reduces hallucinations

What you’ll learn
How embeddings represent “meaning” as vectors
How similarity search retrieves the most relevant chunks
How RAG works: Retrieve → Ground → Generate
How to force the model to answer ONLY from context (grounding)

Models used
Embeddings: Amazon Titan Text Embeddings v2
LLM: Meta Llama 3 Instruct (Bedrock)

Link to Code - https://github.com/aakash1999/AWSBedrockRAG

#aws #awscertification #awsaiPractitioner #aifc01 #examprep
#amazonbedrock #rag #embeddings #semanticsearch #similaritysearch
#machinelearning #generativeai #llama3 #python #cloudcomputing

Видео AWS Bedrock RAG Demo in Python (Embeddings + Similarity Search) | AIF-C01 Ep 6 канала Peace Of Code