Many engineers delay evaluation because they believe a dataset must be large, diverse, and carefully curated before it becomes useful. In reality a small and structured baseline dataset can reveal most early stage failure patterns in a RAG or agent workflow. This guide explains a simple flow that allows you to create that baseline set quickly. It does not rely on scale. It relies on clarity.
1. Start With One Workflow Pick a single workflow instead of the full system. Examples include a retrieval question, a classification step, a routing decision, or a multi turn reasoning path. Narrowing the scope makes evaluation more stable because the expectations are clear and the failure space is smaller.
2. Mine Logs and Repeated User Tasks Logs are often the most natu…
Many engineers delay evaluation because they believe a dataset must be large, diverse, and carefully curated before it becomes useful. In reality a small and structured baseline dataset can reveal most early stage failure patterns in a RAG or agent workflow. This guide explains a simple flow that allows you to create that baseline set quickly. It does not rely on scale. It relies on clarity.
1. Start With One Workflow Pick a single workflow instead of the full system. Examples include a retrieval question, a classification step, a routing decision, or a multi turn reasoning path. Narrowing the scope makes evaluation more stable because the expectations are clear and the failure space is smaller.
2. Mine Logs and Repeated User Tasks Logs are often the most natural source of real examples. They show what users actually tried, what they repeated, and where the system struggled. Look for • repeated queries • failed attempts • examples that required manual correction • patterns that appear across different sessions These logs give you input output pairs with minimal effort, and they represent situations your system truly faces.
3. Create a Small Synthetic Set Synthetic examples fill the gaps that logs do not cover. If logs give you common cases, synthetic items let you add rare or important variations. For example • uncommon phrasing • edge cases • ambiguous requests • expected but missing patterns You do not need many. Even five to ten synthetic samples can surface problems that would otherwise go unnoticed.
4. Validate Structure Before Using the Dataset This is the step most teams skip, yet it often makes the biggest difference. Make sure every sample follows the same structural pattern • same fields • same formatting • same required information • same expected output structure A consistent dataset leads to stable evaluation. Inconsistent structure hides failures and makes improvements impossible to measure.
Why This Flow Works This approach is fast because it removes perfection as a requirement. It is reliable because structural consistency matters more than dataset size. It is practical because logs and synthetic examples complement each other. With this baseline dataset in place, you can • measure improvements • catch regressions • test new workflow designs • compare model candidates • debug without guesswork You now have a grounded way to evaluate your system even in early stages when a gold dataset does not exist.