UX for Enterprise ChatGPT Solutions

UX for Enterprise ChatGPT Solutions

by Richard H. Miller
Released September 2024
Publisher(s): Packt Publishing
ISBN: 9781835461198

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Book description

Create engaging AI experiences by mastering ChatGPT for business and leveraging user interface design practices, research methods, prompt engineering, the feeding lifecycle, and more

Key Features

  • Learn in-demand design thinking and user research techniques applicable to all conversational AI platforms
  • Measure the quality and evaluate ChatGPT from a customer’s perspective for optimal user experience
  • Set up and use your secure private data, documents, and materials to enhance your ChatGPT models
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Many enterprises grapple with new technology, often hopping on the bandwagon only to abandon it when challenges emerge. This book is your guide to seamlessly integrating ChatGPT into enterprise solutions with a UX-centered approach.

UX for Enterprise ChatGPT Solutions empowers you to master effective use case design and adapt UX guidelines through an engaging learning experience. Discover how to prepare your content for success by tailoring interactions to match your audience’s voice, style, and tone using prompt-engineering and fine-tuning. For UX professionals, this book is the key to anchoring your expertise in this evolving field. Writers, researchers, product managers, and linguists will learn to make insightful design decisions. You’ll explore use cases like ChatGPT-powered chat and recommendation engines, while uncovering the AI magic behind the scenes. The book introduces a and feeding model, enabling you to leverage feedback and monitoring to iterate and refine any Large Language Model solution. Packed with hundreds of tips and tricks, this guide will help you build a continuous improvement cycle suited for AI solutions.

By the end, you’ll know how to craft powerful, accurate, responsive, and brand-consistent generative AI experiences, revolutionizing your organization’s use of ChatGPT.

What you will learn

  • Align with user needs by applying design thinking to tailor ChatGPT to meet customer expectations
  • Harness user research to enhance chatbots and recommendation engines
  • Track quality metrics and learn methods to evaluate and monitor ChatGPT's quality and usability
  • Establish and maintain a uniform style and tone with prompt engineering and fine-tuning
  • Apply proven heuristics by monitoring and assessing the UX for conversational experiences with trusted methods
  • Refine continuously by implementing an ongoing process for chatbot and feeding

Who this book is for

This book is for user experience designers, product managers, and product owners of business and enterprise ChatGPT solutions who are interested in learning how to design and implement ChatGPT-4 solutions for enterprise needs. You should have a basic-to-intermediate level of understanding in UI/UX design concepts and fundamental knowledge of ChatGPT-4 and its capabilities.

Table of contents

  1. UX for Enterprise ChatGPT Solutions
  2. Foreword
  3. Acknowledgment
  4. Contributors
  5. About the author
  6. About the reviewers
  7. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
    4. Download the example and files
    5. Conventions used
    6. Get in touch
    7. Share Your Thoughts
    8. Download a free PDF copy of this book
  8. Part 1:UX Foundation for Enterprise ChatGPT
  9. Chapter 1: Recognizing the Power of Design in ChatGPT
    1. Technical requirements
      1. Approach 1 – The no-code approach
      2. Approach 2 – code with Node.JS, Python, or curl
    2. Traversing the history of conversational AI
    3. The importance of UX design for ChatGPT
    4. Understanding the science and art of UX design
      1. The science of design
      2. The art of design
      3. It takes a village to create superb UX
    5. Setting up a customized model
    6. Summary
    7. References
  10. Chapter 2: Conducting Effective User Research
    1. Surveying UX research methods
    2. Understanding user needs analysis
      1. Surveys for conversational AI
      2. Survey checklist
    3. Case study on an effective survey
    4. Designing insightful interviews
      1. Defining research objectives
      2. Selecting participants
      3. Develop a structured interview program
      4. Pilot the interview process and program
      5. Conduct the structured interviews
      6. Record and document findings
      7. Data analysis
      8. Report findings
      9. Summary of the interview process
    5. Getting started with conversational analysis
      1. Tagging a log file should focus on each interaction
      2. Define success and failure categories
    6. Trying conversational analysis
      1. Exploring the examples from the case study
      2. Generate enhancements and bugs from groups of issues
      3. Score results
      4. Results
    7. Summary
    8. References
  11. Chapter 3: Identifying Optimal Use Cases for ChatGPT
    1. Understanding use case basics
      1. Use case or user stories
      2. Establishing a baseline with ChatGPT
      3. Example use case for a ChatGPT instance – patching software
      4. Creating a user story from a use case
      5. Prioritizing ChatGPT opportunities from the use case
    2. Aligning LLMs with user goals
      1. Applications of ChatGPT
      2. Examples of generative AI outside of chat
    3. Avoiding ChatGPT limitations, biases, and inappropriate responses
      1. Lack of real-time information
      2. Complex or specialized topics
      3. Long-form content generation
      4. Long-term memory
      5. Sensitive information
      6. Biased thinking
      7. Emotion and empathy
      8. Ethical and moral guidance
      9. Critical decision making
      10. Programming and debugging
      11. Translation accuracy
      12. Educational substitution
      13. Don’t force-fit a solution
    4. Summary
    5. References
  12. Chapter 4: Scoring Stories
    1. Prioritizing the backlog
      1. WSJF
      2. User Needs Scoring
      3. Scoring enterprise solutions
      4. Examples of scoring
      5. Putting a backlog into order
      6. Patching case study revisited
      7. Extending tracking tools with scoring
      8. Try the User Needs Scoring method
    2. Creating more complex scoring methods
      1. Working with multiple backlogs in Agile
    3. Real-world hiccups with scoring
      1. I know Agile, and this is not WSJF
      2. The use of simple numbers one to four
      3. Weighting factors
      4. Severity seems complicated to judge
      5. The cost is so high that we can’t ever get the work done
      6. Grouping issues into bugs to protect the quality
      7. How to work WSJF into the organization
    4. Summary
    5. References
  13. Chapter 5: Defining the Desired Experience
    1. Designing chat experiences
      1. Chat-only experiences
      2. Integrating ChatGPT into an existing chat experience
      3. Enabling components for a chat experience
      4. Designing hybrid UI/chat experiences
      5. Chat window size and location
      6. Tables
      7. Forms
      8. Charts
      9. Graphics and images
      10. Buttons, menus, and choice lists
      11. Links
    2. Creating voice-only experiences
      1. Designing a recommender and behind-the-scenes experiences
    3. Overarching considerations
      1. Accessibility
      2. Internationalization
      3. Trust
      4. Security
    4. Summary
    5. References
  14. Part 2: Designing
  15. Chapter 6: Gathering Data – Content is King
    1. What is in a ChatGPT foundational model
    2. Incorporating enterprise data using RAG
      1. Understanding RAG
      2. Limitations of ChatGPT and RAG
      3. Building a demo with enterprise data
      4. Cleaning data
      5. Other considerations for creating a quality data pipeline
      6. Resources for RAG
      7. Community resources
    3. Summary
    4. References
  16. Chapter 7: Prompt Engineering
    1. Giving context through prompt engineering
      1. Prompt 101
      2. Designing instructions
      3. Basic strategies
      4. Quick tricks to always keep in mind
      5. A/B testing
    2. Prompt engineering techniques
      1. Self-consistency
      2. General knowledge prompting
      3. Prompt chaining
      4. Program-aided language models
      5. Few-shot prompting
    3. Andrew Ng’s agentic approach
      1. Reflection
      2. Tool use
      3. Planning
      4. Multi-agent collaboration
      5. Advanced techniques
    4. Summary
    5. References
  17. Chapter 8: Fine-Tuning
    1. Fine-tuning 101
      1. Prompt engineering or fine-tuning? Where to spend resources
      2. Token costs do matter
    2. Creating fine-tuned models
      1. Fine-tuning for style and tone
      2. Using the fine-tuned model
      3. Fine-tuning for structuring output
      4. Generating data should still need a check and balance
      5. Fine-tuning for function and tool calling
    3. Fine-tuning tips
    4. Wove case study, continued
      1. Prompt engineering
      2. Fine-Tuning for Wove
    5. Summary
    6. References
  18. Part 3: Care and Feeding
  19. Chapter 9: Guidelines and Heuristics
    1. Applying guidelines to design
    2. Adapting heuristic analysis for conversational UIs
      1. 1 – Visibility of system status
      2. 2 – Match between a system and the real world
      3. 3 – User control and freedom
      4. 4 – Consistency and standards
      5. 5 – Error prevention
      6. 6 – Recognition rather than recall
      7. 7 – Flexibility and efficiency of use
      8. 8 – Aesthetic and minimalist design
      9. 9 – Help users recognize, diagnose, and recover from errors
      10. 10 – Help and documentation
      11. Is there an 11th possible heuristic?
    3. Building conversational guidelines
      1. Web guidelines
      2. A sample guideline set for hybrid chat/GUI experiences
      3. Some specific style and tone guidelines with examples
      4. Flow order can reduce interactions
    4. Case study
      1. Handling errors – repair and disfluencies
    5. Summary
    6. References
  20. Chapter 10: Monitoring and Evaluation
    1. Evaluate using RAGAs
      1. The RAGAs process
      2. Synthesizing data
      3. Evaluation metrics
      4. User experience metrics
      5. Other metrics
      6. Monitoring and classifying the types of hallucination errors
      7. OpenAI’s case study on quality and how to measure it
      8. Systematic testing processes
      9. Testing matrix approach
      10. Improving retrieval
      11. The wide range of LLM evaluation metrics
    2. Monitor with usability metrics
      1. Net Promoter Score (NPS)
      2. SUS
    3. Refine with heuristic evaluation
    4. Summary
    5. References
  21. Chapter 11: Process
    1. Incorporating design thinking into development
      1. Find a sponsor
      2. Find the right tools and integrate Generative AI
      3. Be religious… at first
      4. Avoid “unknown unknowns”
      5. Always evolve and improve
      6. Agile does not mean “no requirements”
      7. Team composition and location matters
      8. Manage Work in Progress (WIP) and technical debt
      9. Focus on customer value
      10. Incorporate the design process into the dev process
    2. Designing a content improvement life cycle
      1. Inputs for conversational AIs
      2. Inputs for recommender UIs
      3. Inputs for backend AIs
      4. Monitoring Monday
      5. Analysis Tuesday (and Wednesday’s workup)
      6. Treatment Thursday and fault-finding Friday
      7. What doesn’t fit into a week is still important
    3. Conclusion
    4. References
  22. Chapter 12: Conclusion
    1. Applying learnings to the new frontier
    2. Double-checking what feels right
      1. Set clear goals
      2. Know your processes
      3. Know the data
      4. Align and be accountable
      5. Prioritize thoughtfully
      6. Automate with intention
    3. Building processes that fit the solution
    4. Wrapping up the journey
    5. References
  23. Index
    1. Why subscribe?
  24. Other Books You May Enjoy
    1. Packt is searching for authors like you
    2. Share Your Thoughts
    3. Download a free PDF copy of this book

Product information

  • Title: UX for Enterprise ChatGPT Solutions
  • Author(s): Richard H. Miller
  • Release date: September 2024
  • Publisher(s): Packt Publishing
  • ISBN: 9781835461198