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Making Your AI Results More Predictable

Making Your AI Results More Predictable

54mIntermediate2024-09-27

Authors

Ronnie Sheer

Ronnie Sheer

Software Developer and Instructor

Course details

Explore the fundamentals of working with AI and large language models, focusing on responsible usage, the intricacies of writing effective user prompts, and how to ensure human oversight while implementing strong, reliable systems. Instructor Ronnie Sheers shows you practical ways to recognize and mitigate biases, hallucinations, and randomness in AI outputs, as well as how to leverage system instructions for improved interaction efficiency. Get started applying few-shot learning techniques to generalize data more effectively. Find out why keeping a human in the loop enhances decision-making and accountability while also ensuring ethical system design through user feedback and risk management. Along the way, learn how to use moderation components, fine-tuning models for specific tasks, semantic similarity searches for better information retrieval, and retrieval-augmented generation (RAG) systems for enhanced system performance.

Skills covered

Responsible AIArtificial Intelligence FoundationsArtificial Intelligence (AI)One-Off

Concepts

0. Introduction

  • 01 - Embracing the unpredictable
  • 02 - What you should know
  • 03 - Warning - Use responsibly
  • 04 - Language model overview
  • 05 - Hallucinations, biases, and randomness

1. Enhancing User Prompts

  • 06 - The value and limitation of processing user prompts
  • 07 - Leveraging system instructions
  • 08 - Few shot learning
  • 09 - Challenge - Use system instructions
  • 10 - Solution - Use system instructions

2. Human Oversight

  • 11 - Keeping a human in the loop
  • 12 - Designing for human oversight
  • 13 - The value of user feedback
  • 14 - Conveying risks to users

3. Implementing Robust Systems

  • 15 - Using moderation components
  • 16 - Fine-tuning models
  • 17 - Semantic similarity search
  • 18 - RAG - Value and drawbacks
  • 19 - Chunking approaches for RAG
  • 20 - Adding re-rank to RAG systems

Conclusion

  • 21 - Staying up-to-date with evolving tech

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