---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: be876332c61797fdf8524f6de6104a55dce5e448dfc74f6c54c4a739f164a6d5
  pageDirectory: concepts
  sources:
    - debug-and-analyze-your-app-with-tracing-databricks-on-aws.md
  confidence: 0.85
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - intermediate-step-instrumentation
    - ISI
  citations:
    - file: debug-and-analyze-your-app-with-tracing-databricks-on-aws.md
title: Intermediate Step Instrumentation
description: Capturing inputs, outputs, and metadata for each intermediate step in a GenAI pipeline (e.g., retrieval, tool calls, LLM interactions) along with user feedback and quality evaluation results.
tags:
  - observability
  - debugging
  - genai
timestamp: "2026-06-19T09:55:35.033Z"
---

# Intermediate Step Instrumentation

**Intermediate Step Instrumentation** refers to the practice of capturing detailed execution data—including inputs, outputs, and metadata—for each discrete step within a GenAI application's pipeline, such as retrieval operations, tool calls, and LLM interactions. This instrumentation enables developers to trace the complete request-response cycle and gain visibility into the internal logic and decision-making processes of complex AI systems. ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## Overview

Modern GenAI applications often consist of multi-step pipelines where a single user request triggers a sequence of operations—retrieving context from a knowledge base, calling external tools, interacting with one or more language models, and assembling a final response. Without instrumentation at each intermediate step, developers have limited visibility into where errors occur, why unexpected outputs are generated, or where performance bottlenecks exist. ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

Intermediate step instrumentation captures the following for each step:

- **Inputs**: The data or context provided to the step (e.g., the query sent to a retrieval system, the prompt passed to an LLM).
- **Outputs**: The result produced by the step (e.g., retrieved documents, tool responses, model completions).
- **Metadata**: Additional contextual information such as latency, token usage, cost, and error details.

## Benefits

### Debugging and Root Cause Analysis

By examining the inputs, outputs, and metadata for each intermediate step, developers can precisely identify where issues or unexpected behaviors occur within complex pipelines. This granular visibility is particularly valuable during development, where it helps uncover what happens beneath the abstractions of GenAI libraries. ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

### Performance Monitoring and Cost Optimization

Intermediate step instrumentation enables tracking of key operational metrics such as latency, cost, and resource utilization at each step of execution. This allows teams to: ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

- Identify performance bottlenecks within complex pipelines.
- Monitor resource utilization to ensure efficient operation.
- Optimize cost efficiency by understanding where resources or tokens are consumed.
- Identify areas for performance improvement in code or model interactions.

### Production Monitoring

In production environments, traces capture errors and operational metrics like latency at each step, aiding in quick diagnostics and real-time issue resolution. ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## Unified Development and Production Experience

A key advantage of intermediate step instrumentation is that it provides a unified experience between development and production. Once an application is instrumented, tracing works consistently in both environments. Developers can navigate traces seamlessly within their preferred environment—whether an IDE, notebook, or production monitoring dashboard—eliminating the need to switch between multiple tools or search through overwhelming logs. ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## Integration with Observability Standards

Intermediate step instrumentation is compatible with OpenTelemetry, an industry-standard observability specification. This compatibility allows trace data to be exported to various services in an existing observability stack, enabling integration with broader monitoring and alerting workflows. ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## Analysis with Natural Language Interfaces

Platforms like [Genie Code](/concepts/genie-code.md) provide natural language interfaces for exploring and debugging instrumented traces. Instead of writing queries or navigating multiple UI pages, developers can ask questions such as "Are there any error traces in this experiment?" or "What's the P95 latency for my traces?" and receive immediate answers. Genie Code has read access to traces, sessions, evaluation runs, scorers, datasets, labeling sessions, and more, enabling a progression from high-level questions to root cause analysis in a single conversation. ^[debug-and-analyze-your-app-with-tracing-databricks-on-aws.md]

## Related Concepts

- [MLflow Tracing](/concepts/mlflow-tracing.md) — The framework for capturing and visualizing trace data.
- [Trace-Based Evaluation](/concepts/mlflow-trace-based-evaluation.md) — Using execution traces for deeper quality analysis.
- [Production Monitoring for GenAI](/concepts/production-quality-monitoring-mlflow-genai.md) — Deploying traces for continuous quality monitoring.
- [User Feedback Collection](/concepts/end-user-feedback-collection-via-sdk.md) — Correlating user feedback with trace data.
- [OpenTelemetry Export](/concepts/mlflow-opentelemetry-metrics-export.md) — Exporting trace data to external observability services.

## Sources

- debug-and-analyze-your-app-with-tracing-databricks-on-aws.md

# Citations

1. [debug-and-analyze-your-app-with-tracing-databricks-on-aws.md](/references/debug-and-analyze-your-app-with-tracing-databricks-on-aws-d9c92247.md)
