What FreshContext is really testing: context judgment before reasoning
FreshContext is built around one question:
What should happen to retrieved context before it reaches the model?
Most RAG and agent systems focus on retrieving more relevant material. That matters, but relevance alone does not tell an AI system whether a source should be trusted, cited, refreshed, verified, used as background, watched, or excluded.
That is the layer FreshContext is testing.
The simple flow is:
candidate context in
decision-ready context out
The current front door is evaluate_context.
It does not fetch, crawl, browse, or scrape by itself. The caller provides candidate context, and FreshContext evaluates it through signals like freshness, provenance, confidence, utility, and source profile.
The main product insight so far:
A context system should not treat all sources the same.
A research paper, job post, official document, finance signal, and social discussion all decay differently. They also deserve different confidence and citation rules.
That is why FreshContext uses Source Profiles.
The goal is not just better ranking.
The goal is better context decisions before model reasoning begins.
I’m especially interested in feedback on three things:
Does “context judgment layer” clearly describe the gap between retrieval and reasoning?
What source types would be most useful to test beyond the current examples?
What would count as credible production-scale validation for this kind of system?
I’m treating this launch as category validation first.
If the category makes sense, the next serious step is larger workload testing, stronger benchmarks, and more real-world pilot examples.
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