Start with Observability

Everything begins with seeing what’s happening under the hood. Observability tools log inputs, outputs, latencies, and metadata, turning black-box LLMs into inspectable systems. This isn’t optional, it’s the foundation for spotting patterns and measuring improvements.

For general apps, track basics like prompt-response pairs and error rates. If your app uses retrieval-augmented generation (RAG) pipelines, layer on RAG-specific metrics: retrieval relevance (does it fetch the right docs?), answer faithfulness (does the output stick to retrieved facts?), and context completeness.

Set this up early to inform later steps like error categorization or testing. For RAG-focused guidance, including metrics and Langfuse integration.

→ Start your observability

→ See Observability in RAG pipelines

Dive into Error Analysis

With observability in place, zoom in on failures. Error analysis involves reviewing traces to classify issues (hallucinations, irrelevance, formatting errors) and uncover root causes. This turns raw logs into actionable insights, prioritizing what to fix next.

For example, filter traces by low user satisfaction scores, tag common failure modes, and cluster similar errors. It’s manual at first but scales with automation, feeding directly into testing and experiments.

→ Error Analysis to Evaluate LLM Applications

Set up Automated Evaluators

In AI development, iterating quickly is important. Manually annotating outputs after every modification is slow and expensive, especially when you want to integrate evaluations into a CI/CD pipeline.

Automated evaluators solve this problem by providing a scalable way to measure and monitor your application’s failure modes, enabling a fast and effective development loop.

→ Automated Evaluations of LLM Applications

Build a Testing Foundation

Now that you’ve identified pain points, formalize tests to prevent regressions. Testing LLM apps blends deterministic checks (e.g., output format) with probabilistic ones (e.g., semantic accuracy via LLM judges).

Testing isn’t exhaustive: focus on high-impact areas. It complements observability by running offline and integrates with the flywheel for continuous validation.

→ Testing LLM Applications

Scale with Synthetic Datasets

Real data is ideal, but it’s often limited. Synthetic datasets fill the gaps: Use LLMs to generate diverse inputs, amplifying your test coverage without waiting for users.

For instance, prompt a model to create query variations, including adversarial ones. This powers robust testing and error simulation, closing the loop from analysis to prevention.

It’s modular: use it when bootstrapping evals or stressing multi-component systems.

→ Synthetic Dataset Generation for LLM Evaluation

Run Experiments and Interpret Results

To quantify progress, compare variants: prompts, models, or pipelines. Experiments measure metrics like accuracy or speed across datasets, revealing winners.

Interpretation is key: Don’t just note “Variant B is 10% better”—analyze why, linking back to error patterns or observability data.

This step ties the flywheel together, turning insights into measurable gains.

→ Experiment Interpretation