add podcast to Chapter 2

README.md

@@ -10,13 +10,14 @@ Please [open an issue](https://github.com/souzatharsis/tamingLLMs/issues) with y
 *Publication Date: February 2, 2025*
 ### *A Practical Guide to LLM Pitfalls with Open Source Software*
 
-Abstract: **The current discourse around Large Language Models (LLMs) tends to focus heavily on their capabilities while glossing over fundamental challenges. Conversely, this book takes a critical look at the key limitations and implementation pitfalls that engineers and technical leaders encounter when building LLM-powered applications. Through practical Python examples and proven open source solutions, it provides an introductory yet comprehensive guide for navigating these challenges. The focus is on concrete problems with reproducible code examples and battle-tested open source tools. By understanding these pitfalls upfront, readers will be better equipped to build products that harness the power of LLMs while sidestepping their inherent limitations.**
+Abstract: *The current discourse around Large Language Models (LLMs) tends to focus heavily on their capabilities while glossing over fundamental challenges. Conversely, this book takes a critical look at the key limitations and implementation pitfalls that engineers and technical leaders encounter when building LLM-powered applications. Through practical Python examples and proven open source solutions, it provides an introductory yet comprehensive guide for navigating these challenges. The focus is on concrete problems with reproducible code examples and battle-tested open source tools. By understanding these pitfalls upfront, readers will be better equipped to build products that harness the power of LLMs while sidestepping their inherent limitations.*
+
 | Chapter                                   | PDF          | Podcast      | Website      | Notebook      | Status               |
 |-------------------------------------------|--------------|--------------|--------------|---------------|----------------------|
 | Preface                   | | | [html](https://www.tamingllms.com/markdown/preface.html) | N/A           | *Ready for Review*                   |
 | About the Book                  | | | [html](https://www.tamingllms.com/markdown/intro.html) | N/A           | *Ready for Review*                   |
 | Chapter 1: The Evals Gap                | [pdf](https://www.dropbox.com/scl/fi/voyhpqp0glkhijopyev71/DRAFT_Chapter-1-The-Evals-Gap.pdf?rlkey=ehzf6g4ngsssuoe471on8itu4&st=zqv98w2n&dl=0)  | [podcast](https://tamingllm.substack.com/p/chapter-1-podcast-the-evals-gap) | [html](https://www.tamingllms.com/notebooks/evals.html) | [ipynb](https://github.com/souzatharsis/tamingLLMs/blob/master/tamingllms/notebooks/evals.ipynb) | *Ready for Review*     |
-| Chapter 2: Structured Output| [pdf](https://www.dropbox.com/scl/fi/x3a84bm1ewcfemj4p7b5p/DRAFT_Chapter-2-Structured-Output.pdf?rlkey=zysw6mat7har133rs7am7bb8n&st=4ns4ak24&dl=0) | podcast | [html](https://www.tamingllms.com/notebooks/structured_output.html) | [ipynb](https://github.com/souzatharsis/tamingLLMs/blob/master/tamingllms/notebooks/structured_output.ipynb) |    |
+| Chapter 2: Structured Output| [pdf](https://www.dropbox.com/scl/fi/x3a84bm1ewcfemj4p7b5p/DRAFT_Chapter-2-Structured-Output.pdf?rlkey=zysw6mat7har133rs7am7bb8n&st=4ns4ak24&dl=0) | [podcast](https://tamingllm.substack.com/p/chapter-2-podcast-structured-output) | [html](https://www.tamingllms.com/notebooks/structured_output.html) | [ipynb](https://github.com/souzatharsis/tamingLLMs/blob/master/tamingllms/notebooks/structured_output.ipynb) |  *Ready for Review*  |
 | Chapter 3: Managing Input Data | | | [html](https://www.tamingllms.com/notebooks/input.html) | [ipynb](https://github.com/souzatharsis/tamingLLMs/blob/master/tamingllms/notebooks/input.ipynb) |     |
 | Chapter 4: Safety              | | | [html](https://www.tamingllms.com/notebooks/safety.html)  |  [ipynb](https://github.com/souzatharsis/tamingLLMs/blob/master/tamingllms/notebooks/safety.ipynb) |     |
 | Chapter 5: Preference-Based Alignment     | | | [html](https://www.tamingllms.com/notebooks/alignment.html) | [ipynb](https://github.com/souzatharsis/tamingLLMs/blob/master/tamingllms/notebooks/alignment.ipynb) |     |

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

@@ -115,7 +115,7 @@
     "A common use case where document parsing matters is structured data extraction, particularly in the presence of complex formatting and layout. In this case study, we will extract the economic forecasts from Merrill Lynch's CIO Capital Market Outlook released on December 16, 2024 {cite}`merrill2024`. We will focus on page 7 of this document, which contains several economic variables organized in a mix of tables, text and images (see {numref}`forecast`).\n",
     "\n",
     "\n",
-    "```{figure} ../data/input/forecast.png\n",
+    "```{figure} ../_static/input/forecast.png\n",
     "---\n",
     "name: forecast\n",
     "alt: Forecast\n",
@@ -1519,7 +1519,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = md_llm.convert(\"../data/input/forecast.png\")"
+    "result = md_llm.convert(\"../_static/input/forecast.png\")"
    ]
   },
   {

tamingllms/_build/html/notebooks/input.html

@@ -1358,7 +1358,7 @@ <h3><a class="toc-backref" href="#id300" role="doc-backlink"><span class="sectio
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">md_llm</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">&quot;../data/input/forecast.png&quot;</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">md_llm</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s2">&quot;../_static/input/forecast.png&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>

tamingllms/_build/jupyter_execute/markdown/intro.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "02a05cbc",
+   "id": "dfab2d09",
    "metadata": {},
    "source": [
     "(intro)=\n",

tamingllms/_build/jupyter_execute/notebooks/input.ipynb

@@ -115,7 +115,7 @@
     "A common use case where document parsing matters is structured data extraction, particularly in the presence of complex formatting and layout. In this case study, we will extract the economic forecasts from Merrill Lynch's CIO Capital Market Outlook released on December 16, 2024 {cite}`merrill2024`. We will focus on page 7 of this document, which contains several economic variables organized in a mix of tables, text and images (see {numref}`forecast`).\n",
     "\n",
     "\n",
-    "```{figure} ../data/input/forecast.png\n",
+    "```{figure} ../_static/input/forecast.png\n",
     "---\n",
     "name: forecast\n",
     "alt: Forecast\n",
@@ -1519,7 +1519,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = md_llm.convert(\"../data/input/forecast.png\")"
+    "result = md_llm.convert(\"../_static/input/forecast.png\")"
    ]
   },
   {