📚 Nexus guide: response optimization

See how response optimization helps you cut token costs, reduce clutter, and make your AI workflows work smarter.

Civic Team

What is response optimization?

Response optimization is the process of refining and restructuring tool outputs before they reach your Large Language Model (LLM). Many tools return large, verbose payloads—deep JSON objects, full HTML documents, extensive metadata—that the model doesn’t actually need.

Without optimization, this extra data can:

Increase token costs
Consume valuable context window space
Introduce noise that reduces model accuracy

Response optimization ensures the LLM receives only the most relevant, streamlined data so your workflows remain efficient and reliable.

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Why this matters

1. Lower token usage

By reducing the size of each response, you directly reduce LLM processing costs.

Technique	What it does
Field filtering (whitelisting)	Selects only the fields required for your workflow and removes everything else.
JSON-to-CSV conversion	Converts repetitive JSON arrays into compact CSV, often reducing token volume by more than 90%.

2. Better use of the model’s context window

Smaller, cleaner responses leave more room for multi-step reasoning and longer-running workflows.

Technique	What it does
Content simplification	Converts verbose HTML into clean Markdown that is easier for the model to parse.
Payload reduction	Frees up additional context window capacity so the LLM can maintain more state and integrate more data.

How Nexus optimizes responses

Nexus makes response optimization simple by applying these transformations automatically, without requiring custom scripts or manual cleanup.

With Nexus, you can:

Filter and reshape tool outputs before they reach the LLM
Convert formats (e.g., JSON → CSV, HTML → Markdown) in a single step
Remove unnecessary fields and metadata to minimize payload size
Ensure consistent, predictable response structures across different tools

The result is a clean, efficient data pipeline that reduces costs, improves accuracy, and enables more scalable workflows.

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