When something breaks in a traditional software system, you usually know it. There is an error message, an exception, a crash log pointing to a specific line. The system tells you where to look.
AI systems do not work that way. They do not crash. They just give bad outputs. And that makes debugging significantly harder, because the failure is silent.
This distinction is also one of the most common questions that comes up in AI engineer interviews: If your AI system is producing bad outputs, what would you do first?
There is no one-line answer. But there is a structured approach.
Start with Observability
Before you can fix anything, you need to be able to see everything.
Every prompt going into your system and every output coming out needs to be visible and traceable. Tools like LangSmith are built specifically for this. They let you trace every step in the workflow, what prompt went in, what the model returned, how long the call took, how many tokens were used, and where the chain broke down.
Without this visibility, debugging an AI system is guesswork. With it, you at least know what actually happened.
Once you can see the full workflow, start asking targeted questions:
- Is the prompt clear and specific enough?
- Is there an input-output mismatch somewhere in the chain?
- Is the model hallucinating facts that are not in the context?
- Did a tool call fail or return an unexpected response?
- Is the agent taking the wrong reasoning path at a decision point?
The answer to each of these points to a different fix. Which is why tracing comes before everything else.
Debugging RAG Systems Specifically
RAG-based systems have an additional layer of structure when it comes to debugging, because the failure can live in one of two places: Retrieval or Generation.
The way to separate them is to inspect the retrieved context for each query directly.
If the retrieved documents are irrelevant, incomplete, or missing key information, the problem is in Retrieval. This usually points to issues in how the content was chunked, how the embeddings were generated, how reranking is working, or how the vector search is configured.
If the retrieved context looks accurate but the model is still producing incorrect or hallucinated responses, the problem is in Generation. This is caused by prompt quality, reasoning failures, or limitations in the model itself.
That distinction matters because the fix for a retrieval problem and the fix for a generation problem are completely different. Collapsing them into one undifferentiated “the system is broken” makes both harder to solve.
Evaluation Comes After Tracing
Once you have traced the failure and identified where it is coming from, the next step is evaluation.
Useful metrics at this stage include:
- Faithfulness — is the response grounded in the retrieved context or is it generating beyond it?
- Relevance — does the response actually address the query?
- Groundedness — are the claims in the output supported by the source material?
- Latency — is the system slow in a way that points to an inefficiency in the chain?
Frameworks like Ragas and LLM-as-a-Judge are widely used for evaluating these at scale, particularly when manual review of every output is not practical.
The Debugging Framework in Practice
The approach that works consistently across different AI system types is straightforward:
Trace → Evaluate → Identify → Fix
Trace the full workflow to see exactly what happened. Evaluate the outputs against measurable criteria. Identify whether the failure is in the prompt, the retrieval, the generation, or the tool layer. Fix that specific component, version the change, and trace again.
The shift happened for me during the AI Agents and Autonomous Systems module in be10x’s AI Career Accelerator. The module included hands-on work with a RAG-based customer support system, and at some point I was staring at outputs that were wrong and had to actually figure out why. Not follow a walkthrough. Actually trace it, find the failure point, and fix it. That experience is what made the framework real. I understand these systems now in a way I did not before, and that understanding came from doing, not from reading about it.
Frequently Asked Questions
What is the first thing to do when an AI system gives bad outputs?
Start with observability. Use a tracing tool like LangSmith to see every step of the workflow before you try to fix anything.
How is debugging a RAG system different from a regular LLM?
RAG systems have two distinct failure points: retrieval and generation. You need to inspect the retrieved context first to determine which one is the actual source of the problem.
What tools are used for evaluating AI system outputs?
Ragas and LLM-as-a-Judge are among the most widely used frameworks. They help measure faithfulness, relevance, and groundedness at scale.
What does LangSmith do specifically?
LangSmith is a tracing and observability tool for LLM applications. It logs every prompt, response, token count, latency, and tool call so you can see exactly what happened at each step of your chain.
