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

# How-To: Launch a New Context

> Create and release a new recommendation surface with context-specific logic, recipe, pipeline, and measurement.

Use this guide when launching recommendations into a new surface, such as a homepage feed, related articles rail, product detail page, checkout upsell, video carousel, or email digest.

## Setup

<Steps>
  <Step title="Define the business goal">
    Decide what the surface should improve: CTR, conversion, AOV, retention, long-tail discovery, editorial coverage, or content freshness.
  </Step>

  <Step title="Create a dedicated context">
    Open **Console > Contexts**, create a context, and set the recommendation type, model family, and retrieval algorithm.
  </Step>

  <Step title="Attach a ranking recipe">
    Choose a blank recipe, use-case preset, or reference template. Tune candidate sources, signals, discovery, rerank behavior, and guardrails.
  </Step>

  <Step title="Review the pipeline">
    Open the context's Pipeline tab after saving. Confirm candidate limits, scoring, rules, and post-processing fit the surface.
  </Step>

  <Step title="Add filters and rules">
    Use Advanced for hard pre-query filters. Use the Rules Engine for post-scoring business logic.
  </Step>
</Steps>

## Validate context behavior in staging

* Call the Core API recommendation endpoint with the new `context_id` and representative user IDs.
* Compare output quality with and without the context applied.
* Check that the right item categories, availability constraints, and business rules are reflected.
* Use [Explainability](/guides/explainability) to inspect retrieval score, rules, pipeline stages, and feature contributions.
* Confirm the context's **Currently serving** model is the one you expect.

## Roll out progressively

* Start with a small traffic segment or internal-only surface.
* Keep context-specific instrumentation so results can be measured independently.
* Watch the context's analytics against the business goal.
* Increase rollout percentage only after quality checks pass.

## Keep context docs operational

For each context, maintain a short owner note with:

* purpose of the surface
* target KPI
* attached recipe
* key filters or rules
* expected model family
* rollout status

This makes future recipe, pipeline, and rule changes easier to audit.

## Example app journey: checkout upsell launch

<Steps>
  <Step title="Define upsell objectives">
    Merchandising defines checkout upsell objectives, such as increasing AOV without recommending unavailable or already-owned items.
  </Step>

  <Step title="Create the checkout context">
    Team creates a checkout context with checkout-safe filters and a conversion-oriented recipe.
  </Step>

  <Step title="Review the pipeline">
    Operators check candidate limits, page size, and whether the rules stage is enabled.
  </Step>

  <Step title="Request checkout recommendations">
    Backend requests recommendations using the checkout `context_id` during checkout.
  </Step>

  <Step title="Tag checkout events">
    Events are tagged from checkout interactions and posted to `/v1/events`.
  </Step>

  <Step title="Evaluate lift">
    Analytics is filtered by context to evaluate lift before increasing traffic.
  </Step>
</Steps>

## Next steps

* [Contexts overview](/contexts)
* [Console Contexts guide](/guides/contexts)
* [How-To: Measure Recommendation Performance](./how-to-measure-recommendation-performance)