Feedback and Tuning

Feedback and tuning are how your project gets smarter over time. Digest Engine does not just collect content and rank it once. It also learns from the choices your team makes as you review articles, themes, and related signals.

The more consistently you give useful feedback, the better the system can align future recommendations with the actual editorial focus of the project.

Why Feedback Matters

Every project starts with an initial understanding of what it is trying to cover. That first understanding is useful, but it is only a starting point.

Real editorial judgment emerges after your team begins interacting with the content:

• approving strong matches
• rejecting weak ones
• promoting useful themes
• dismissing irrelevant suggestions
• reviewing entities that matter to the project

These actions give the system concrete signals about what belongs inside the project’s focus and what does not.

What Counts as Feedback

Feedback is not limited to one button or one screen. In practice, the system can learn from several kinds of editorial decisions, including actions such as:

• marking content as a strong fit or a poor fit
• approving or rejecting entity candidates
• promoting or dismissing themes and ideas
• reinforcing the kinds of items that match the project well

Each of these actions helps refine the way the project understands relevance.

What the System Learns From Feedback

At a high level, Digest Engine uses your feedback to sharpen the project’s sense of what good content looks like.

That includes learning more about:

• the subjects that truly match the project
• the kinds of sources that produce useful material
• the entities and themes that matter most
• the borderline content that should rank lower in the future

This is what turns the project from a generic content feed into something that better reflects your team’s editorial standards.

The Project’s Underlying Topic Model

Under the hood, the project maintains an internal representation of what it is about. You can think of this as the project’s evolving editorial center of gravity.

When you give positive feedback, you strengthen the system’s confidence in content that resembles those approved examples. When you give negative feedback, you help the system move away from patterns that do not belong.

You do not need to manage that model directly. What matters for users is the practical result: better future ranking when your feedback is clear and consistent.

What Good Feedback Looks Like

The most useful feedback is selective, intentional, and consistent.

Good feedback usually means:

• approving content that is clearly an excellent example of the project focus
• rejecting content that is clearly off-topic or not useful
• avoiding casual positive feedback on items that are merely acceptable
• reinforcing the editorial patterns you genuinely want more of

If everything gets a positive signal, the project has less ability to distinguish the strongest matches from the mediocre ones.

When You Should Expect Results

Feedback improves future behavior, but it does not usually rewrite the past.

In practical terms:

• the biggest impact is usually on newly ingested or newly evaluated content
• content you already reviewed may not be instantly reshuffled everywhere
• improvements become more visible as fresh material enters the project

This means tuning works best as an ongoing habit rather than a one-time cleanup. Small, steady feedback tends to produce better long-term results than waiting and trying to correct everything in one session.

What Feedback Is Best For

Normal project feedback works very well for improving nuance.

Use it when you want to:

• tighten the relevance boundary around a well-defined topic
• reduce recurring false positives
• reinforce the kinds of stories your editors consistently choose
• help the system better understand the tone or focus of the project

This is the right tool for refinement.

When Feedback Is Not Enough

Feedback is less effective when the project itself has fundamentally changed.

For example, if a project was originally set up for one broad topic and your team now wants it to cover a much narrower or entirely different space, incremental tuning may not be enough to produce a clean result.

That kind of shift is not just a refinement. It is a change in editorial mission.

In those cases, you may need to:

• redefine the project more clearly
• rethink the sources feeding it
• reset expectations about what the project should cover
• consider starting a new project if the focus has changed dramatically

Feedback is powerful, but it works best when the project still has the same core purpose.

A Practical Tuning Workflow

Teams usually get the best results from tuning when they build it into their normal review process.

1. Review incoming content in Projects and Content.
2. Give strong signals on the clearest good and bad matches.
3. Review important entities in Entities and Authority.
4. Promote or dismiss emerging themes in Themes and Trends.
5. Watch how future content quality changes over time.

This keeps feedback connected to real editorial decisions rather than turning it into a separate maintenance task.

What Success Looks Like

When feedback and tuning are working well, you should gradually notice that:

• stronger content rises more consistently
• irrelevant items appear less often
• theme suggestions feel more aligned with the project
• entity and authority signals become more useful
• drafting a newsletter requires less cleanup and filtering

That is the real goal of tuning: not just changing scores, but reducing friction across the whole editorial workflow.