Debugging Large Language Models (LLMs) — Challenges, Tools & Modern Techniques Explained
Debugging Large Language Models (LLMs) is completely different from debugging traditional software. Unlike normal code, LLMs are non-deterministic, opaque, and driven by massive datasets — which makes finding and fixing issues far more complex.
In this video, we break down:
🔍 Key Challenges in Debugging LLMs
Non-determinism: Same input → different outputs
Opaque reasoning: Hard to interpret internal decision-making
Massive parameters & training data: Difficult to trace errors
Evaluation ambiguity: Outputs aren’t always clearly right or wrong
Distribution shift: Bugs appear only in real-world usage
🛠️ Modern Approaches to LLM Debugging
Prompt tracing & system logging
Automated evaluation & synthetic test cases
Model behavior probing & interpretability tools
Input attribution & influence functions
Fine-tuning, data curation, and RLAIF
🧰 Popular Debugging & LLMOps Tools
LangSmith
Trulens
Promptfoo
Mechanistic interpretability tools from OpenAI & Anthropic
If you're building AI agents or LLM-powered apps, this video will help you understand how to diagnose issues, improve reliability, and adopt modern LLMOps workflows.
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