Why Early Testing Matters
Most GenAI projects start with a quick proof of concept. Early demos perform great, but once scaled to enterprise reality, things begin to break: answers become inconsistent, token usage spikes and complexity increases
This is where early tracing and testing of LLM calls (already during the prototype and integration phase) makes all the difference. It provides a factual basis instead of gut feeling:
- Misalignments (the difference between performed and expected result) are revealed before they become expensive
- Architecture and model choices are made based on data.
- Teams gain faster time to market by fixing issues early.
The Right Tools: Monitoring, Logging, Testing
A robust setup relies on three pillars:
- Monitoring: Tools like Langfuse, MLflow, or Weights & Biases track token usage, costs, and performance.
- Testing: Frameworks such as DeepEval or Bedrock Evaluation ensure reproducible test runs with clear criteria.
- Logging: While often overlapping with monitoring, logging focuses on traceability. Using frameworks like LangChain logging, every retrieval and generation step becomes reproducible and transparent, making it possible to understand why a specific output was produced.
Together, they answer key questions:
- Which query or task was initiated – and by which agent?
- Which data source or tool was retrieved or invoked?
- Which model generated the final output – and at what cost?
- How did agent decisions or tool calls influence the result?
Data: Why a Test Dataset Is Non-Negotiable
Generic tests don’t tell you much. What you need is a domain-specific test dataset that reflects your business context.
A practical starting point: about 30 representative Q&A pairs from your business domain. From there, grow the dataset continuously by incorporating subject matter expert (SME) feedback.
Choosing the Right Metrics
Early on, you don’t need dozens of KPIs — but you do need the right ones. Defining metrics forces clarity on what actually matters:
- Answer quality: semantic correctness, not just word overlap.
- Retrieval quality: Recall@k to ensure the right documents are surfaced.
- Token consumption and costs: Are we spending efficiently?
- Model comparison: Which LLM delivers the most stable results for your case?
Common Issues Early Tracing Will Expose
- Inconsistent answer quality – detecting hallucinations and irrelevant outputs.
- Retrieval errors – missing or mis-ranking key documents.
- Cost drivers – uncontrolled token usage leading to budget blowouts.
- LLM benchmarking – revealing that one model may be more stable than another.
- Agent orchestration – identifying which agent in a multi-agent setup delivered the wrong result.
Conclusion: Testing Early Pays Off
Early tracing and testing of LLM calls is not overhead. It is an investment that pays for itself many times over:
- You avoid costly rework at the end of the project.
- You expose weaknesses in RAG pipelines when they are still easy to fix.
- You create transparency for decisions on architecture, cost, and performance.
- You sharpen business understanding of what the application is truly expected to deliver, aligning technical output with real user and stakeholder needs.
At HMS, we aim to start a GenAI project without at least a minimal test and monitoring setup. Even a small dataset of 30 questions is enough to establish a baseline and avoid blind spots. This ensures every proof of concept can evolve into a stable, scalable system.
Key Takeaways
- Monitoring, logging, and testing must start from day one – not after go-live.
- Involve business experts early: Testing becomes truly effective once domain experts can easily add new test samples and refine metrics.
- A small, domain-specific dataset is enough to begin – then expand it with SME feedback.
- Early metric definition sharpens both technical and business problem understanding.
- Transparency reduces costs and accelerates delivery by aligning teams on measurable outcomes.
