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Noting in 4K
Noting in 4K

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Learnings from GenAI on AWS at Deloitte workshop

I attended an in-personal workshop provided by Deloitte and AWS for NZ TechWeek24 on 22nd of May, noted down some key points that I probably can learn further with hands-on projects later.

Key concepts:

  • LLMOps
  • Considerations for shortlisting LLMs
  • Hallucination & Retrieval-Augmented Generation (RAG) pattern
  • Embeddings
  • Conversational Buffer Memory
  • Prompt Engineering Techniques
  • Fine Tuning (just lightly touched)

Some use cases in Deloitte we went through:

  • Customer support GenAI POC - understand customer query, extract relevant parts, draft email/slack responses (100% consistency of response msgs), and then provides links to knowledge base - 25% decreased request handling time
  • Knowledge Base Summarisation for Chorus - more to read
  • Query Structured Data from internal supported vector data store - using the same stack/tools we used in labs below

The stack and tools we used in the labs:

  • Python boto3
  • Amazon Bedrock - fully managed service for using foundation models from Amazon and third parties
  • LangChain - Python and JS libraries, provides convenient functions for interacting with Amazon Bedrock’s models and related services like vector databases
  • Streamlit - quickly creates web UI from Python without much frontend skills, great for POCs (Streamlit API Reference)
  • Amazon Titan Embeddings - converts natural language text into numerical representations for later use cases such as searching or comparing semantic similarity

If you are interested in GenAI on AWS, there are a few skill builder free labs for AI Readiness to explore around.

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