RAG Debugging Field Manual

Stop Guessing. Start Measuring. Fix Your RAG System.

The Problem You're Facing

• Your RAG system works... sometimes.
• Demo to stakeholders? Perfect answers.
• Production with real users? Hallucinates pricing from the wrong product tier.
• Add new documentation? Recall mysteriously drops 20%.
• User asks about refunds? Chatbot invents a policy that doesn't exist.

You add a reranker. It gets worse. You tweak the prompt. It helps one query, breaks five others.
You're debugging blind.

What You'll Learn

This is a systematic debugging approach based on semantic stress measurement (ΔS)
- a technique from academic research that I've adapted for production RAG systems.

In 12 modules, you'll learn:

Stop Hallucinations
- Semantic firewall: One 10-line filter that catches 60%+ of hallucinations
- ΔS measurement: The metric that actually predicts when your LLM will make shit up
- Residue detection: Catch "keyword match, wrong intent" failures (like confusing "Pro plan" with "Basic plan")
- Collapse detection: Know when your LLM is drifting from the source material before it answers

Fix Retrieval
- When recall is actually the problem (and when it's not)
- Reranker reality check: The $400/month mistake most teams make
- Hybrid retrieval: When BM25 + dense actually helps vs. when it's cargo-culting
- The 0.85 rule: Why recall@50 ≥ 0.85 is your go/no-go threshold

Make It Production-Ready
- Citation integrity: Auto-detect when facts get merged across sources
- Data contracts: JSON schemas that make every run reproducible
- Golden test sets: Queries that catch regressions before users do
- Worked examples: Real bugs, real ΔS values, real fixes with before/after metrics

This is for your if:

• You're a software engineer building RAG systems (not ML theory)
• You're stuck debugging why answers are wrong
• You need measurable diagnostics, not vibes
• You're using LangChain, LlamaIndex, or custom Python/TS stacks
• You want **code you can copy-paste today**

Not for you if:

• You've never built a RAG system (start with tutorials first)
• You're looking for ML theory (this is practical engineering)
• You want to understand transformers (wrong course)

Why Trust This?

I'm Jon, a Senior Software Engineer
I build RAG systems for many sectors with complex requirements.
When your system processes decisions that affect real people, hallucinations are dangerous.

This course is the debugging methodology I developed through months of fixing production RAG failures in high-stakes environments where "just try a different model" isn't an option and doesn't actually work.

The techniques inside - ΔS measurement, semantic firewalls, collapse detection - are what I use when debugging RAG systems today.

This is what works when users are depending on correct answers.

What You Get

12 core modules across 410-pages in a comprehensive debugging manual (Use like a reference jump to what you need)

Core Modules (250 pages):
- Module 0-6: Foundation & immediate fixes
- Start with Module 1 (15 min read)
- Jump to your specific symptom

Advanced Topics (150 pages):
- Module 7-11: Pipeline engineering, testing, real examples
- Read when you need deeper optimization

Quick Reference (60 pages):
- Appendix A: Formula lookup
- Appendix B: Decision flowcharts
- Appendix C: Emergency troubleshooting

Pricing $29 $9 (Launch pricing—limited time)

Why so cheap?
I want this in the hands of every engineer debugging RAG systems. Once I hit 50 sales, price goes to $29.

Money-back guarantee:
If you don't find at least one actionable fix in the first 3 modules, email me and I'll refund you. No questions asked.

FAQ

Q: Is this specific to any particular framework?
A: No. The debugging techniques (ΔS, semantic firewall, collapse detection) work with any RAG stack—LangChain, LlamaIndex, custom pipelines. I show implementations in both Python and TypeScript.

Q: What if I'm using a different embedding model?
A: The techniques work with any model (OpenAI, Cohere, sentence-transformers, etc.). Just needs L2-normalized embeddings for cosine similarity.

Q: Do I need a PhD in ML?
A: No. If you've built a RAG system, you have the background. This is practical software engineering, not ML theory.

Q: What if my problem isn't covered?
A: Module 1 has a decision tree that maps symptoms to modules. Appendix C has emergency troubleshooting. If you're truly stuck, email me and I'll help diagnose.

Q: Do I get updates?
A: Yes. All future updates to the field manual are free. I'm actively working on RAG systems and will add new debugging patterns as I discover them.

Q: What if I hate it?
A: Email me within 30 days for a full refund. Keep the field manual as a PDF.

What you're really buying:
- The next time your RAG system hallucinates in production, you'll know exactly what to measure
- The next time you're debugging at 2am, you'll have flowcharts that get you to root cause in 30 seconds
- The next time someone says "just add a reranker," you'll know whether that's actually the problem

Launch price ends at 50 sales. After that, it's $29.

Clean looking guide

Tonnes of practical code snippets