Building Pipeline Insight Capture for Better ETL Debugging: Part 1

14 Jul, 2025

Here's the thing about running ETL pipelines manually... You learn stuff every single time, but then you forget it. I'll finish processing an episode of The Game Podcast, notice that speaker matching worked way better than usual, and think, "I should remember this." Then three weeks later, I'm staring at terrible speaker results, wondering what I did differently last time.

This drives me crazy. I'm sitting on over 115 episodes of processed audio (more than 800 episodes to go), and I keep making the same mistakes because I don't capture what I learn from each run.

So I'm building something about it. Not for users or customers, but for me. Because I'm vibe-coding this whole project with Claude Code, and the bottleneck isn't adding features, it's making the pipeline more robust and learnable.

You can download the complete implementation document: Implementation Plan - CLI Post-Run Insight Capture Markdown File

_It includes everything: JSON schemas, CLI interface designs, pipeline integration code, automation layers, and even a future roadmap for LLM-powered insight analysis. It's the kind of prompt I write when I'm thinking through all the edge cases before you start coding with Claude Code.)

The Problem I'm Solving

Every time I run my pipeline, I learn something:

"Oh, episodes over 45 minutes always timeout in the embedding stage."
"When there are 3+ speakers, diarization gets messy, but the confidence scores tell the story"
"That Alex Hormozi episode had perfect speaker matching - what was different?"

But I don't write any of this down. I just... remember some of it, forget most of it, and repeat the same debugging cycles over and over.

I need a system that captures insights while they're fresh. Not system logs (I have those). Not performance metrics (I have those too). Human insight. The "aha" moments that only happen when you're actually watching the pipeline run.

What I'm Building

I'm creating a CLI tool that prompts me after every pipeline run to reflect and capture key learnings. Think of it as a post-run interview with myself.

The basic questions are simple:

What worked well in this run?
What didn't work or felt fragile?
What did you learn from this run?
What should be fixed, tweaked, or refactored?
What metric would help you track this next time?
Any new questions to explore?

But here's where it gets interesting for podcast processing specifically. I'm adding advanced questions that target the exact failure modes I see:

Stage-by-stage diagnostics: Did speech-to-text, diarization, embedding, speaker matching, and topic segmentation all complete successfully? What was the quality like?
Confidence assessment: Overall run confidence (high/medium/low). Do you know if there are any signs of drift compared to previous runs? Silent failures suspected?
Customer trust check: Would I confidently share this output with someone else? Can I explain how I got these results?

This isn't generic ETL stuff. It's tuned for the specific problems I hit when processing podcast audio.

The Technical Plan

I'm implementing this in phases because I've learned not to over-engineer on the first pass. Here's the high-level roadmap (the whole plan linked above has way more detail):

Phase 1-2: Basic CLI tool with the core questions. Interactive prompts, markdown output with YAML frontmatter for structure.

Phase 3-4: Develop an automation layer that operates in various modes. Sometimes I want complete reflection, sometimes I just want to capture the basics and move on.

Phase 5: Polish and production readiness. Git integration, error handling, the boring but necessary stuff.

Phase 6 (Weeks 11-14): This is where it gets really useful. Search and retrieval across all my past insights. "Show me the last 5 timeout issues," or "What patterns do I see in embedding drift?"

Phase 7 (Maybe later): LLM-powered assistant that can answer questions like "What causes speaker matching to fail?" by analyzing my entire insight corpus.

The key insight here is that Phase 6 is more important than Phase 7. I need to be able to find and summarize my past learnings before I need an AI to interpret them.

The complete plan breaks down each phase into specific tasks, includes code examples, and even has a "regrets" section based on common ETL team mistakes. It's the kind of planning document I wish I had for other projects.

Why This Matters for Manual Pipelines

Here's what I realized: most pipeline tooling assumes you're running things automatically in production. But I'm running manually, iterating constantly, learning from each run. The traditional approach of "just look at the logs" doesn't capture the human insight that happens during manual execution.

I know when something feels off before the metrics show it. I notice patterns across episodes that logs don't reveal. I make manual adjustments based on intuition that I want to remember and systematize.

This tool is designed for that workflow. It's not trying to replace monitoring or alerting. It's trying to capture the knowledge that only exists in my head after I watch a pipeline run.

The YAML Frontmatter Strategy

Each insight file will start with structured metadata:

---
run_id: run-20250714-1034
timestamp: 2025-07-14 10:34
status: success
duration: 823s
episode: "Alex Hormozi – Ep. 902"
git_sha: abc123def
stages_completed: ["transcription", "diarization", "embedding", "speaker_matching", "topic_segmentation"]
performance_metrics:
  stt_avg_confidence: 0.87
  speaker_match_rate: 0.94
  embedding_drift_score: 0.12
---

This isn't just metadata for the sake of it. It's what makes Phase 6 (search and retrieval) possible. I can query "show me all runs where speaker_match_rate < 0.8" or "find episodes over 800 seconds that had embedding issues."

What I Expect to Learn

I'm honestly not sure what patterns I'll discover. That's the whole point. I suspect I'll find:

Specific episode length thresholds where things break
Speaker count patterns that predict diarization quality
Seasonal or topical trends in processing difficulty
Config changes that I thought helped but actually didn't

But I might be completely wrong. The beauty of capturing insights systematically is that you discover things you weren't looking for.

The Retrieval Game-Changer

Phase 6 is what I'm most excited about. Once I have 50+ insight files, I can start asking questions like:

"What were the last 5 timeout issues and how did I fix them?"
"Show me all the episodes where embedding drift was high"
"What patterns do I see in successful runs vs. problematic ones?"

This transforms the tool from "digital diary" to "learning system." Instead of just capturing insights, I can build on them.

I'm writing this before I've built anything because I want to document the journey, not just the results. I'm curious about the patterns I'll find, whether my planned questions are the right ones, and how this will impact my relationship with the pipeline.

Plus, if you're running manual ETL processes, you might recognize this frustration. Maybe you'll try something similar, or maybe you'll tell me I'm overthinking it. Either way, I'm interested in the conversation.

– Benoit Meunier