update intro

tamingllms/_build/html/_sources/markdown/intro.md

@@ -37,7 +37,7 @@ Throughout this book, we'll tackle the following (non-exhaustive) list of critic
 
 4. **Hallucination Management**: These models can generate plausible-sounding but entirely fabricated information, creating significant risks for production applications.
 
-5. **Safety and Security**: LLMs can generate harmful, biased, or inappropriate content, requiring robust safeguards and monitoring systems to ensure safe deployment.
+5. **Safety and Alignment**: LLMs can generate harmful, biased, or inappropriate content, requiring robust safeguards and monitoring systems to ensure safe deployment.
 
 6. **Cost Optimization**: The computational and financial costs of operating LLM-based systems can quickly become prohibitive without careful management, observability and optimization.
 
@@ -46,21 +46,43 @@ Throughout this book, we'll tackle the following (non-exhaustive) list of critic
 
 ## A Practical Approach
 
-This book takes a hands-on approach to these challenges, providing:
+This book takes a hands-on approach to these challenges, with a focus on accessibility and reproducibility. 
+All examples and code are:
+
+- Fully reproducible and documented, allowing readers to replicate results exactly
+- Designed to run on consumer-grade hardware without requiring expensive hardware
+- Available as open source Python notebooks that can be modified and extended
+- Built using free and open source tools accessible to everyone
+- Structured to minimize computational costs while maintaining effectiveness
+
+## An Open Source Approach
+
+Throughout this book, we'll leverage open source tools and frameworks to address common LLM challenges. In that way, we are prioritizing:
+
+- **Transparency**: Open source solutions provide visibility into how challenges are being addressed, allowing for better understanding and customization of solutions.
+- **Flexibility**: Open source tools can be modified and adapted to specific use cases, unlike black-box commercial solutions.
+- **Cost-Effectiveness**: Most of open source tools we will cover are freely available, fostering accessibility and reducing costs.
+- **Vendor Independence**: Open source solutions reduce dependency on specific providers, offering more freedom in architectural decisions.
+
+## Open Source Book
+
+In keeping with these open source principles, this book itself is open source and available on GitHub. It's designed to be a living document that evolves with the changing landscape of LLM technology and implementation practices. Readers are encouraged to:
+
+- Report issues or suggest improvements through GitHub Issues
+- Contribute new examples or solutions via Pull Requests
+- Share their own experiences and solutions with the community
+- Propose new chapters or sections that address emerging challenges
+
+The repository can be found at https://github.com/souzatharsis/tamingllms. Whether you've found a typo, have a better solution to share, or want to contribute an entirely new section, your contributions are welcome.
 
-- Concrete Python examples that you can run and modify
-- Real-world scenarios and solutions
-- Testing strategies and best practices
-- Cost optimization techniques
-- Integration patterns and anti-patterns
 
 ## A Note on Perspective
 
 While this book takes a critical look at LLM limitations, our goal is not to discourage their use but to enable more robust and reliable implementations. By understanding these challenges upfront, you'll be better equipped to build systems that leverage LLMs effectively while avoiding common pitfalls.
 
 The current discourse around LLMs tends toward extremes—either uncritical enthusiasm or wholesale dismissal. This book takes a different approach:
 
-- **Practical Implementation Focus**: Rather than theoretical capabilities, we examine real-world challenges and their solutions.
+- **Practical Implementation Focus**: Rather than theoretical capabilities, we examine practical challenges and their solutions.
 - **Code-First Learning**: Every concept is illustrated with executable Python examples, enabling immediate practical application.
 - **Critical Analysis**: We provide a balanced examination of both capabilities and limitations, helping readers make informed decisions about LLM integration.
 
@@ -70,7 +92,7 @@ This book is intended for Software Developers taking their first steps with Larg
 
 This book is designed for: 
 
-- Software Engineers building LLM-powered applications 
+- Software/AI Engineers building LLM-powered applications 
 - Technical Product Managers leading GenAI initiatives 
 - Technical Leaders making architectural decisions
 - Open Source advocates and/or developers building LLM Applications 
@@ -79,7 +101,7 @@ This book is designed for:
 
 Typical job roles:
 
-- Software Engineers building AI-powered platforms
+- Software/AI Engineers building AI-powered platforms
 - Backend Developers integrating LLMs into existing systems
 - ML Engineers transitioning to LLM implementation
 - Technical Leads making architectural decisions
@@ -122,8 +144,8 @@ Before diving into the examples in this book, you'll need to set up your develop
 ### Python Environment Setup
 ```bash
 # Create and activate a virtual environment
-python -m venv llm-book-env
-source llm-book-env/bin/activate  # On Windows, use: llm-book-env\Scripts\activate
+python -m venv taming-llms-env
+source taming-llms-env/bin/activate  # On Windows, use: taming-llms-env\Scripts\activate
 
 # Install required packages
 pip install -r requirements.txt
@@ -157,8 +179,8 @@ Now that your environment is set up, let's begin our exploration of LLM challeng
 
 ## About the Author(s)
 
-Dr. Tharsis Souza is a computer scientist and product leader specializing in AI-based products. He is a Lecturer at Columbia University's Master of Science program in Applied Analytics, Head of Product, Equities at Citadel, and former Senior VP at Two Sigma Investments.
+Dr. Tharsis Souza is a computer scientist and product leader specializing in AI-based products. He is a Lecturer at Columbia University's Master of Science program in Applied Analytics, (*incoming*) Head of Product, Equities at Citadel, and former Senior VP at Two Sigma Investments. He also enjoys mentoring under-represented students & working professionals to help create a more diverse global AI ecosystem.
 
-With over 15 years of experience delivering technology products across startups and Fortune 500 companies globally, Dr. Souza is also an author of numerous scholarly publications and is a frequent speaker at academic and business conferences. Grounded on academic background and drawing from practical experience building and scaling up products powered by language models at early-stage startups, major institutions as well as advising non-profit organizations, and contributing to open source projects, he brings a unique perspective on bridging the gap between LLMs promised potential and their practical limitations using open source tools to enable the next generation of AI-powered products.
+With over 15 years of experience delivering technology products across startups and Fortune 500 companies, Dr. Souza is also an author of numerous scholarly publications and is a frequent speaker at academic and business conferences. Grounded on academic background and drawing from practical experience building and scaling up products powered by language models at early-stage startups, major institutions as well as advising non-profit organizations, and contributing to open source projects, he brings a unique perspective on bridging the gap between LLMs promised potential and their practical implementation challenges to enable the next generation of AI-powered products.
 
 Dr. Tharsis holds a Ph.D. in Computer Science from UCL, University of London following an M.Phil. and M.Sc. in Computer Science and a B.Sc. in Computer Engineering.

tamingllms/_build/html/_sources/notebooks/alignment.ipynb

@@ -2385,6 +2385,13 @@
     "evals_df_results.to_csv(\"../data/alignment/evals_df_results.csv\", index=False)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results show that the aligned model has a higher mean score than the base model, indicating that it is more aligned with the policy. The aligned model also has a higher percentage of responses that are aligned with the policy, indicating that it is more aligned with the policy."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 143,
@@ -2498,6 +2505,20 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Discussion\n",
+    "\n",
+    "- how large should the DPO dataset be?\n",
+    "- how to choose your base model?\n",
+    "- on limitations of synthetic data generation\n",
+    "- on limitations of using LLM as a judge for evaluation\n",
+    "- how safe is safe enough?\n",
+    "- split between bad prompts and helpful prompts -> model becoming apologetic for bad prompts"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

tamingllms/_build/html/_sources/notebooks/output_size_limit.ipynb

@@ -467,7 +467,7 @@
     "\n",
     "## Conclusion\n",
     "\n",
-    "In conclusion, while managing output size limitations in LLMs presents significant challenges, it also drives innovation in application design and optimization strategies. By implementing techniques such as context chunking, efficient prompt templates, and graceful fallbacks, developers can mitigate these limitations and enhance the performance and cost-effectiveness of their applications. As the technology evolves, advancements in contextual awareness, token efficiency, and memory management will further empower developers to build more robust and scalable LLM-powered systems. It is crucial to stay informed about these developments and continuously adapt to leverage the full potential of LLMs while addressing their inherent constraints.\n",
+    "In conclusion, while managing output size limitations in LLMs can be challenging, it also drives innovation in application design and optimization strategies. By implementing techniques such as context chunking, efficient prompt templates, and graceful fallbacks, developers can mitigate these limitations and enhance the performance of their applications. As the technology evolves, advancements in contextual awareness, token efficiency, and memory management will further mitigate these limitations, empowering developers to build more robust and scalable LLM-powered systems.\n",
     "\n",
     "\n",
     "## References\n",