---
title: "Best Practices for Using Cookbooks"
slug: "best-practices-for-using-cookbooks"
updated: 2026-01-20T16:00:36Z
published: 2026-01-20T16:00:36Z
canonical: "docs.sightmachine.com/best-practices-for-using-cookbooks"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sightmachine.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Best Practices for Using Cookbooks

## Run Settings

- Set **Top Runs** significantly lower than the total available runs to ensure meaningful comparisons.
- Define **run boundaries carefully**to capture true production periods—especially for continuous processes.
  - Use **shift changes** for historical analysis.
  - Use **campaign-based boundaries** (e.g., flavor, color, packaging) where applicable.
  - If no boundary exists, add a field called **production date** to the pipeline and use **day** as a boundary condition.
- Align run quantification with the client’s process (e.g., wheel type or unit of production).

## Conditions

- Choose condition fields that result in a **sufficient number of runs per recipe**—avoid over-bucketing.
- Avoid creating too many conditions, as this can lead to recipes with **insufficient data points.**
- Do not presuppose conditions (e.g., shift, flavor, speed) unless requested—validate run settings first.
- Skip product selection during cookbook creation unless products truly differ in outcomes or logic.
  - Instead, configure **product as a condition** when appropriate. Instead, configure **product as a condition**when appropriate.
    - **If there are other significant conditions,** use product selection to avoid too many factorial outcomes in conditions.
- Use uncontrollable variables (e.g., humidity, wind speed) as **conditions or filters**, not levers.
  - Use wisely, or you will end up with too many conditional options due to a factorial approach. **Remove any conditions that have minimal impact.**

## Outcomes

- Use **top-level KPIs** (e.g., OEE) as primary outcomes when deploying cookbook insights to production.
- Add secondary KPIs (e.g., quality, efficiency) as **0-weight outcomes** if needed for insight.
- Ensure outcomes are realistic, production-aligned, and defined with **specific, normalized metrics**
- Do not select metrics which are optimized by switching plant off.  (e.g., decrease steam)
- Confirm outcome metrics are not better suited as **conditions or filters**.

## Levers

- Start broadly, then **narrow to 20–30 meaningful levers**to avoid confusion.
  - Consider creating two cookbooks: one for engineers with more levers, and one for operators, where 20–30 levers is typically the maximum they should see.
- Use **lever insights** to identify and retain the most influential levers.
- Remove levers that never change, cannot be controlled by operators, or represent duration, counters, or status values.
  - Levers can be determined if they can be changed typically through conversation with SMEs, PID loop tagging such as PV vs SP, or shape of data
- Common effective levers include **temperature, pressure, and setpoints.**
- Levers should be operator-controllable, variable, and directly influential on production outcomes.

## Filters

- Use filters to exclude unwanted data (e.g., unsafe or noncompliant runs).
- Apply filters **before or after scoring runs** as appropriate to control what data appears in results.

## Limits & Alerts

- Configure limits to reflect **realistic standard deviations**, warnings, and alarms.
  - The SD limits can also be changed, as well as the numerical values. Numerical minima and maxima can be hard-set (e.g., to limit to a new safety parameter).
- Adjust limits based on natural process variability, operator and engineer input, and technical documentation or machine manuals.
  - **Spend sufficient time on this**—frequent alerts on highly variable fields will quickly erode operator trust in the cookbook.
- Well-tuned limits ensure alerts are **meaningful and actionable.**

## Date Range Selection

- Limit historical data to periods **after major plant updates or overhauls** to prevent outdated processes from skewing results.