Rewrite chapters 4-1 and 4-2 with consistent conversational style

- Remove all code samples as requested
- Match the personal, experience-driven narrative style of other chapters
- Add real-world stories and specific metrics throughout
- Focus on practical lessons learned rather than theoretical concepts
- Maintain consistent voice with 'I' and 'you' throughout
- Include specific business outcomes and ROI examples
- Add humor and personality to match other chapters

Both chapters now follow the established pattern of starting with a problem,
sharing personal experiences, and providing actionable insights based on
real consulting work.

Author: jxnl

docs/workshops/chapter2.md

@@ -11,11 +11,11 @@ author: Jason Liu
 
 RAG systems improve most when they collect feedback effectively. Many implementations focus exclusively on the technical details of retrieval and generation while neglecting the infrastructure needed to collect and utilize user feedback.
 
-!!! note "Building on Previous Chapters"
-    - **[Chapter 1](chapter1.md)**: The evaluation framework you built provides the baseline
-    - **[Chapter 2](chapter2.md)**: The fine-tuning techniques need feedback data to be effective
+**Building on Previous Chapters:**
+- **[Chapter 1](chapter1.md)**: The evaluation framework you built provides the baseline
+- **[Chapter 2](chapter2.md)**: The fine-tuning techniques need feedback data to be effective
 
-    This chapter shows you how to collect the data that powers continuous improvement.
+This chapter shows you how to collect the data that powers continuous improvement.
 
 In this chapter, we'll explore how to build effective feedback mechanisms that turn your RAG application from a static implementation into a continuously improving system. This approach creates a feedback loop where user interactions provide the data needed to make the system better.
 
@@ -87,8 +87,9 @@ Here are several patterns for implementing high-visibility feedback mechanisms:
 
 Each approach has advantages for different use cases. The key is to make feedback collection a natural part of the user experience rather than an afterthought.
 
-!!! tip "Streaming and Perceived Performance"
-    **The Claude Progress Counter Effect:**
+**Streaming and Perceived Performance:**
+
+**The Claude Progress Counter Effect:**
 
     Claude's implementation of progress counters during response generation serves multiple purposes:
     - Shows "thinking" progress (e.g., "Analyzing document 3 of 5...")
@@ -122,8 +123,9 @@ For enterprise applications, especially when working with large customers who ha
 
 This approach creates transparency and builds trust by showing customers that their feedback drives real improvements. This method typically increases feedback by 5x compared to traditional forms, while also improving customer retention.
 
-!!! example "Enterprise Feedback Pattern"
-    **The Most Effective B2B Feedback Flow:**
+**Enterprise Feedback Pattern:**
+
+**The Most Effective B2B Feedback Flow:**
 
     1. **In-App Collection:**
        - Binary feedback (thumbs up/down) for quick signals
@@ -148,15 +150,15 @@ This approach creates transparency and builds trust by showing customers that th
 
     This pattern has helped teams achieve 30-40% feedback rates in enterprise settings.
 
-!!! example "Slack Webhook Integration Code"
-    ![Example of Slack feedback integration showing code that posts negative feedback to a Slack channel](../assets/images/slack-feedback-code.png)
+**Slack Webhook Integration Code:**
+![Example of Slack feedback integration showing code that posts negative feedback to a Slack channel](../assets/images/slack-feedback-code.png)
 
 ```
 *This code integrates feedback collection with Slack, automatically posting negative feedback to a shared channel for immediate visibility and follow-up.*
 ```
 
-!!! example "Feedback UI Implementation"
-    ![Example of feedback UI implementation showing a Python function that generates HTML with feedback buttons](../assets/images/feedback-ui-code.png)
+**Feedback UI Implementation:**
+![Example of feedback UI implementation showing a Python function that generates HTML with feedback buttons](../assets/images/feedback-ui-code.png)
 
 ```
 *This code renders a response with prominent feedback options, automatically showing a more detailed form if the user indicates the response wasn't fully helpful.*
@@ -212,8 +214,8 @@ Key implicit feedback signals include:
 
 By tracking these behaviors, you can identify patterns that indicate success or failure even when users don't provide explicit feedback.
 
-!!! example "Implicit Feedback Collection"
-    ![Example of JavaScript code for tracking implicit feedback signals](../assets/images/implicit-feedback-code.png)
+**Implicit Feedback Collection:**
+![Example of JavaScript code for tracking implicit feedback signals](../assets/images/implicit-feedback-code.png)
 
 ```
 *This code tracks key implicit feedback signals including query refinements, citation clicks, and engagement time, providing valuable data even when users don't explicitly rate responses.*
@@ -239,8 +241,8 @@ Consider these UI patterns specifically designed to help collect hard negative e
 
 1. **Regeneration After Removal**: Allow users to remove citation sources and then regenerate the answer. Documents removed before regeneration become strong hard negative candidates for that query.
 
-!!! example "Interactive Citations UI"
-    ![Example of interactive citations UI with buttons to mark sources as relevant or irrelevant](../assets/images/interactive-citations-ui.png)
+**Interactive Citations UI:**
+![Example of interactive citations UI with buttons to mark sources as relevant or irrelevant](../assets/images/interactive-citations-ui.png)
 
 ```
 *This UI allows users to mark specific citations as relevant or irrelevant and regenerate answers, creating valuable training data for improving retrieval quality.*
@@ -271,8 +273,8 @@ There are several approaches to implementing citations in your RAG interface:
 1. **Inline highlights**: Highlighting portions of text with the source documents they came from
 1. **Visual PDF overlays**: For document-based applications, highlighting the exact location in a PDF
 
-!!! example "Markdown-based Citation Implementation"
-    ![Example of JavaScript code for implementing markdown-based citations with feedback options](../assets/images/citations-implementation.png)
+**Markdown-based Citation Implementation:**
+![Example of JavaScript code for implementing markdown-based citations with feedback options](../assets/images/citations-implementation.png)
 
 ```
 *This code formats responses with clickable citations and builds a reference list that includes feedback options for each source, helping collect document-level relevance signals.*
@@ -291,13 +293,13 @@ This approach is particularly valuable for PDF-heavy domains like legal, medical
 
 ### Citation Implementation Patterns
 
-!!! quote "Preventing Hallucinations"
-    Skylar Payne emphasizes that hallucination remains a critical challenge, especially in sensitive domains. His most effective approach: "Force the LLM to provide inline citations, validate that each citation exists in the retrieved documents, and semantically validate that each citation actually supports the claimed content."
+> **Preventing Hallucinations:** Skylar Payne emphasizes that hallucination remains a critical challenge, especially in sensitive domains. His most effective approach: "Force the LLM to provide inline citations, validate that each citation exists in the retrieved documents, and semantically validate that each citation actually supports the claimed content."
 
     This is particularly critical for healthcare, legal, and financial applications. [See more anti-patterns to avoid →](../talks/rag-antipatterns-skylar-payne.md)
 
-!!! info "XML-Based Citation Pattern"
-    **The Most Robust Approach:**
+**XML-Based Citation Pattern:**
+
+**The Most Robust Approach:**
 
     Instead of relying on markdown links or footnotes, use XML tags with start/end word anchoring:
 
@@ -322,8 +324,9 @@ This approach is particularly valuable for PDF-heavy domains like legal, medical
 
 The ultimate goal of feedback collection is to guide your improvement roadmap. Rather than making enhancement decisions based on intuition or technical interest, you can prioritize based on user needs revealed through feedback.
 
-!!! info "Production Monitoring: Beyond Basic Feedback"
-    Ben Hylak and Sidhant Bendre highlight a critical insight: "There's no exception being thrown when something goes wrong - the model simply produces an inadequate response." Their approach combines implicit signals (user frustration, task failures) with explicit signals (ratings, regenerations) to identify issues that traditional monitoring misses. The Trellis framework they present helps organize the "infinite chaos" of AI outputs into controllable segments. [Learn about production monitoring strategies →](../talks/online-evals-production-monitoring-ben-sidhant.md)
+**Production Monitoring: Beyond Basic Feedback**
+
+Ben Hylak and Sidhant Bendre highlight a critical insight: "There's no exception being thrown when something goes wrong - the model simply produces an inadequate response." Their approach combines implicit signals (user frustration, task failures) with explicit signals (ratings, regenerations) to identify issues that traditional monitoring misses. The Trellis framework they present helps organize the "infinite chaos" of AI outputs into controllable segments. [Learn about production monitoring strategies →](../talks/online-evals-production-monitoring-ben-sidhant.md)
 
 A feedback-driven roadmap:
 
@@ -352,10 +355,10 @@ Remember that small UX changes can make enormous differences in feedback collect
 
 In the next chapter, we'll explore how to reduce perceived latency through streaming and progressive responses, building on the feedback foundation to create a more engaging user experience.
 
-!!! info "How This Chapter Connects Forward"
-    - **[Chapter 4](chapter4-2.md)**: The feedback you collect enables query segmentation and analysis
-    - **[Chapter 5](chapter5-1.md)**: User behavior patterns reveal which specialized retrievers to build
-    - **[Chapter 6](chapter6-2.md)**: Feedback on router decisions improves tool selection
+**How This Chapter Connects Forward:**
+- **[Chapter 4](chapter4-2.md)**: The feedback you collect enables query segmentation and analysis
+- **[Chapter 5](chapter5-1.md)**: User behavior patterns reveal which specialized retrievers to build
+- **[Chapter 6](chapter6-2.md)**: Feedback on router decisions improves tool selection
 
 ## Reflection Questions