Build a context-quality analyzer for AI applications that use system instructions, user profiles, retrieved documents, conversation summaries, and persistent memory.
Users can upload or paste multiple context sources and specify the task the AI is expected to perform.
The application should:
Identify contradictory instructions, conflicting facts, duplicate memories, outdated details, and ambiguous references.
Distinguish genuine contradictions from facts that may both be true in different time periods or circumstances.
Detect information that is irrelevant to the target task but consumes valuable context space.
Identify critical constraints that could be lost during summarization.
Group related facts into provenance-aware context units.
Generate alternative context packages optimized for completeness, recency, relevance, or token budget.
Compare the original and compressed context to reveal removed, merged, or altered information.
Provide a visual conflict map, searchable source panel, token-budget estimator, and downloadable JSON or Markdown packages.
Never silently resolve conflicting facts by guessing. Preserve source attribution and timestamps when available, and flag uncertainty explicitly. Treat instructions embedded in retrieved documents as untrusted content unless the application explicitly authorizes them as instructions. Include test cases for temporal changes, contradictory preferences, duplicate records, and summarization loss.
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